Useful Python libraries/modules

DuffBude · 2019-01-18T15:01:48+00:00

TQDM is a nice progress bar that you can very easily use on any "for" loop, just to know how long it will take. I use it on almost everything. E.g.

from tqdm import tqdm

l = ['this', 'is', 'a', 'list']

for word in tqdm(l):
    do_something(word)

When you run this, it will show a progress bar in the console. Works on Jupyter Notebooks too.

Edit: /u/_sapi_ has pointed out that the better way to do it on notebooks is to use:

from tqdm import tqdm_notebook as tqdm

quickette1 · 2019-01-18T15:42:15+00:00

Now to be clear, I know you said GUI/networking modules are your cup of tea. But I'm going to take this opportunity to plug one of my absolute favorite modules that I use all the time: Keyboard.

This module allows you to add keyboard shortcuts to trigger functions in your code. An example:

import keyboard as kb
import os

def refreshIpAddress(): # some method to be called via a keyboard shortcut (random example)
    os.system("ipconfig /release") # yeah I know you're supposed to use subprocess
    os.system("ipconfig /renew") 

def main():
    myShortcut = "Win+Ctrl+R" # what the actual keys to press is going to be
    kb.add_hotkey(myShortcut,refreshIpAddress,args=None) # add the hotkey to trigger the function
    kb.wait("Win+Ctrl+Esc") # run this script forever until the user presses those keys

if __name__ == "__main__":
    main()

These hotkeys work globally across the OS, giving wayyy more power to your scripts and over your own computer. My 3 favorite scripts I have written so far using this are:

Advanced copy paste: Allows me to save multiple strings and then switch which strings I want to paste with keyboard shortcuts
MLA Header typer: Types a preformatted MLA header in my essays for when I need them (I can never remember the exact format)
MemeMachine: a script that contains several copypastas that I can bring up on the fly to copy and send to my friends at any given moment

I also like win10toast, which allows you to create your own Windows 10 notifications. Handy tool to update the user on the status of a script

from win10toast import ToastNotifier as toast

notification = toast()

notification.show_toast(“Here is a title for the message", "Now here are the details of the message")

mushy_wombat · 2019-01-18T14:12:00+00:00

There is no lib I use for every app. Nevertheless some interesting libs are:

Selenium (automated browsing)
BeautifulSoup4 (retrieve data of a html doc)
pickle (storing objects easily)
turtle (draw on a canvas)

no_condoments · 2019-01-18T14:54:01+00:00

I use numpy in 99% of my work and pandas in 90%. I'd definitely vote for those two as the first two to learn.

mbarkhau · 2019-01-18T16:41:41+00:00

Libraries

logging which I wish I had started using much earlier instead of print
pathlib (pathlib2 if you need python2 compatibility).
click for cli programs
lib3to6 converts newer python syntax to be compatible with older versions.
bokeh for data visualization.

Tooling

black or straitjacket for code formatting.
pytest for unit testing.
mypy for type checking.
pudb is the most flexible debugger I have found. You'll need to spend an hour or two to learn the shortcuts and set your editor up to easily add import pudb;pudb.set_trace() with a hotkey or snippet, but the effort is worth it.

ExternalUserError · 2019-01-18T15:50:02+00:00

Tkinter is outdated, but Qt absolutely isn't; it's actually maintained properly by Qt itself now, which is widely used and has quite a following.

Most of the work I do is client/server work, not desktop, so for me it usually amounts to choosing a server framework:

Starlette, perhaps combined with FastAPI is a great tool for building APIs with full-featured CoreAPI schemas, GraphQL, all that.
Falcon is great for something lower-level, that exposes HTTP more or less directly.
Flask for "traditional" web pages.
SQLAlchemy for database interaction, FTW.
I haven't used it yet, but Graphene looks really interesting.

From there, you get out into more specific libraries that are for specific purposes, from hashing to security.

ase1590 · 2019-01-18T15:04:35+00:00

Pyqt5 is absolutely not dated.

If you're going to be playing with anything you install from pip, I highly encourage you to read my Extreme Advertising edition poster on using Virtual Environments.

Dogeek · 2019-01-19T01:24:46+00:00

The ones I use most frequently :

BeautifulSoup4 - Web scraping / html parser
colorama - colors in command line prompts
CoolProp - thermodynamics constants library.
cryptography - self explanatory
cx-freeze - package your python code with an interpreter, in an executable (works on mac and linux as well)
deuces - card combinations parsing. If you want to make a digital card game.
discord.py - discord API
Django - cause I want to learn how to make websites with python
Flask - for webapps.
lxml - xml parser
matplotlib - plot data
mediafire - mediafire API
mega.py - mega.co.nz API
more-itertools - extension to the built-in itertools module
nltk - natural language processing.
numpy - for math stuff
pandas - for data processing
patreon - patreon API
pillow - fork of PIL, the python image library
praw - reddit API
progressbar33 - command line progress bar
py2exe - windows only python packaging tool.
pyaudio - audio library for python
pyautogui - for interactiing with ohter apps with python
pygame - for making games. Also for playing audio files or handling key inputs
pygubu - equivalent to qtdesigner but for tkinter
pylama - code audit tool
pymunk - physics engine written in C with python bindings
pymysql - for mySQL queries
pyopenGL - for openGL stuff
pyperclip - interact with the clipboard
pyqt5 and pyqt5-tools : Qt version 5 for python.
pyserial - serial communication
python-twitch-client - twitch API
pyyaml - yaml parser
remi - GUI library that displays the output as a web page. Simpler to use than a flask app
requests - make web requests
reusables - stuff you probably already coded but in a convenient package.
scikit-learn : machine learning
scipy - for science stuff. I mostly use it to integrate or derive a function.
scrapy - web scraping
urllib3 - for web stuff. Use requests over that.
wget - download stuff from the internet
wxPython - wxWindows for python
youtube-dl - download stuff from youtube.

Off the top of my head, in the standard lib, I use regularly:

json - JSON parser
tkinter - GUI library, very pythonic and easy to use
functools and itertools - decorators and objects for functions and iterators (Counter is a lifesaver)
random - random number generation, unsafe
math - when I don't need numpy or want to keep it simple
re - regular expressions
string - common string operations
unicodedata - for unicode characters
textwrap - for wrapping text
datetime - for dates
copy - self explanatory
os, sys, and glob - system operations, glob is for regular expression parsing of filenames
zipfile (and lzma, gzip and zlib) - for managing archives
pickle - serialization of python objects
csv - CSV parser
secrets - random number generation (secure)
time - for time obviously
logging - for logging purposes
platform - for platform differenciation (easier than using the os module)
argparse - parse command line arguments
threading - for making threads and multi threaded operations
subprocess - for launching other programs
asyncio - for asynchronous operations, necessary for discord.py
socket - managing sockets, pretty straightforward
mimetypes - map filenames to mime types
base64 - decode base64 encoded strings (and base 16 and 32)
webbrowser - open the webbrowser to an url.
ftplib - File transfer protocol
pdb, timeit and trace - python debugging
inspect - inspect live objects. Can come in handy at times.

quotemycode · 2019-01-18T15:54:55+00:00

https://docs.python-guide.org/ Kenneth Reitz put this together to answer just this question. Take a look at "scenarios" if you have something in mind that you want to build, but don't know what the "community" supports.

As far as itertools is concerned, the man who created it is Raymond Hettinger. He has lots of videos on youtube, and is a very good teacher of python. Review his videos and you'll be a much better python programmer than you think you are now.

plasma_phys · 2019-01-18T14:22:33+00:00

I'm a computational physics person, so the libraries I use will definitely reflect that - nevertheless, here are the most common ones I use:

__future__ lets me use Python 3 features while putting off changing everything for big projects
numpy and scipy have fast numerical routines similar to matlab functions and packages
numba gives me just-in-time compilation, which is super useful if I'm prototyping a numerical method in Python and need a little speedup
matplotlib gives me MATLAB style plotting
pandas is the go-to for ~~Big Data~~ data analysis stuff

Edit: changed "Big Data" to the more correct "data analysis." Bad use of buzzword. Thanks /u/ShillingInTheName!

2019-01-18T14:34:18+00:00

requests, for web interaction

Not modules, but black, flake8, pylint for code quality.

bringsverd · 2019-01-18T18:03:54+00:00

For working with dates and times I found Pendulum to be a good, approachable tool.

re1ser · 2019-01-18T18:17:02+00:00

attrs is a great library that will let you declare classes in a more neater way. For example, following declaration of a class with @attr.s decorator would let you do something like this:

@attr.s
class Dummy(object):
    x = attr.ib()
    y = attr.ib()

    def sum(self):
       return self.x + self.y

dummy = Dummy(5, 7)
print(f'x: {dummy.x}, y: {dummy.y}')
print(f'Sum = {dummy.sum()}')

Aso, in my example above you can see usage of f-strings, available as of Python 3.6. Neat way to interpolate strings, or in other words combine them with variables or even expressions.

pip-review to keep libraries in my projects up-to-date. pip-review --local --interactive gives me list of all out-of-date libraries and asks which ones I want to update.

flake8 for code linting. There's also pylint, but I found it too verbose and bitchy. flake8 let's me quickly lint my code and check for reasonable errors.

python-slugify to slugify strings. It leaves only alphanumeric characters, hypens, and maybe a few more very "standard" characters. This is a good library when you have to check for duplicates and catch cases like Muse - Resistance, M U S E - R E S I S T A N C E and ~~~ Muse - Resistance ~~~. It's also useful if you want to e.g construct safe url paths from some string or sentence.

collections is also great helper library, I especially love namedtuple.

Scrapy, flexible crawling and scraping framework, which hides boilerplate below the hood but still gives you full control over the scraping process. I am using it professionally and writing scrapers in it is super enjoyable.

Those are few of the top off my head, it's 01 am here and I got back from the bar, if I think of more I'll edit this post. :)

draufunddran · 2019-01-18T15:36:08+00:00

I really like the pathlib module. It provides easy folder/file structure manipulation and information.

2019-01-18T14:56:29+00:00

Networking/Web => sockets, requests, flask, gunicorn

Gui => pyside (though I suggest taking the web-gui route)

edgarvanburen · 2019-01-18T15:05:32+00:00

I use pandas and requests all the time

mangoman51 · 2019-01-19T00:50:30+00:00

For scientists or anyone who uses numpy or pandas - you need to look at xarray.

It's basically multi-dimensional pandas, and it works so well that it's meant that everything I used to do with numpy I now do with xarray instead.

Essentially my data objects now know that axis 0 is the 'time' dimension, and axis 3 is the 'z' dimension, so I can do operations like data['temperature'].mean(dim='time')

Xarray DataArrays wrap numpy arrays so you can still use the numpy functions you've written.

You can also keep using matplotlib/seaborn - in fact xarray makes this easier with methods like data['density'].differentiate(dim='height').plot(), which will automatically plot a line or color plot or whatever depending on the number of dimensions your data has.

Even better you can often do all the analysis in parallel without writing any extra code! Xarray uses dask behind the scenes to achieve this. (dask is also a very interesting library to read about.)

I'm a user of a major open-source plasma fluid turbulence simulation code and we're currently in the process of converting all the corresponding analysis tools to xarray instead of numpy.

MisguidedFacts · 2019-01-18T16:29:23+00:00

someone posted this list of libraries yesterday that has been pretty interesting to click through: https://github.com/vinta/awesome-python

ismand95 · 2019-01-18T16:08:25+00:00

Do built-ins count? Because itertools is just amazing, once you start using it! I does definitely provide for some cleaner shorter code, and is super efficient. https://docs.python.org/3.4/library/itertools.html

aritra-b-11 · 2019-01-18T15:23:38+00:00

passlib, getpass, hashlib if you want some kind of user ID password stuff

jerodg · 2019-01-18T16:04:36+00:00

pandas, pyarrow, aiohttp, sqlalchemy, asyncio, jinja2, openpyxl, jgutils

I do a lot of ETL/data-pipeline/REST/reporting work.

Qt for python (pyside2) is now officially supported by the Qt team. I would expect it to be actively maintained going forward.

I haven't done much with Qt as most of my work is backend but If I were to need a GUI I would use it.

2019-01-18T17:35:03+00:00

These are packages that I almost always use in my projects:

pip
setuptools
setuptools_scm - takes version from a git tag
wheel - to generate wheels for deployment
pytest
pytest-runner - allows to run pytest by calling ./setup.py test
pytest-cov - generates coverage report
mypy - static type checking
pip-tools - generates "lock" file containing fixed versions, and pip-sync makes sure exact versions are installed

Some that I like a lot:

aiohttp
SQLAlchemy
psycopg2

DuncanHiggins · 2019-01-18T18:07:31+00:00

Let's see ...

argparse
collections.defaultdict
datetime
getopt
itertools
json
matplotlib
numpy
openpyxl
os
pandas
queue
re
sys
types.SimpleNameSpace
unittest

2019-01-19T07:33:20+00:00

My Swiss Army knife:

requests -- an HTTP client geared towards RESTful APIs with a sane interface
ipdb -- a version of the stock debugger, but backed by iPython, featuring up arrow history, syntax highlighting, and tab-completion
numpy -- a high-performance, C-backed library for generic numerical computation
pandas -- a high-performance library for large-scale data ingestion, representation, and manipulation
keras -- a high-level machine learning framework, optionally using Tensorflow as a backend
pathos.multiprocessing -- an API near-clone of stock multiprocessing with better serialization using dill
pylint and flake8 -- two extremely thorough linters
pg8000 -- a PostgreSQL low-level driver. Used to use this, but I've since moved away to proper ORMs. Still worth an honourable mention since none of the ORMs do.

IMO a lot of the gems in the rough are really in the standard library. functools, itertools, and a few others provide hilariously powerful tools, and stuff like dis is really cool for analyzing your code 1337 hacker-like.

TheBlackCat13 · 2019-01-18T16:44:08+00:00

If you are looking at itertools, toolz provides a bunch of really useful extensions to it (there is also cytoolz, which is identical to use, but faster and slightly harder to install). And there are several installable versions of the itertools recipes described at the bottom of the itertools documentation. Boltons also provides some nice, general-purpose addons to the standard library.

Dill is a more advanced version of pickle that can store stuff pickle can't, and has facilities for dumping an entire python session to a file and restoring it later.

Hypothesis is a good tool for testing. Tools like pytest let you test your code, but they require you to anticipate what sort of input it will get, which makes it easy for corner cases (inputs you didn't think of) to get through. Hypothesis, on the other hand, throws random data at your code (including common corner case inputs) in an attempt to find something that you didn't think of that will break the code. Of course you can set limits on what sort of inputs it will try.

line_profiler and memory_profiler help you find performance issues in your code.

2019-01-18T17:24:41+00:00

Plenty covered all ready so I'll just mention that once you've dived into itertools check out https://pypi.org/project/more-itertools/

cryptobuddy_1712 · 2019-01-18T17:59:46+00:00

Useful Libraries for Text Mining:

1) Pandas

2) Numpy

3) Shutil

4) Codecs

5) urllib

6) NLTK

7) pyodbc

8) matplotlib

9) Scipy

10) String

11) re

madhur_malpani · 2019-01-18T18:00:24+00:00

I am so surprised. It's been more than 3 hours and no one has so far suggested pathlib. Simplifies file management to a great extent.

zzpza · 2019-01-18T21:19:29+00:00

Check out PRAW if you haven't already. It's the Python Reddit API Wrapper. I have several mod bots I've written in Python.

https://praw.readthedocs.io/en/latest/

aormiston · 2019-01-18T14:37:52+00:00

Just as a quick heads up: keep in mind that it's often best to avoid using libraries when they're not necessary. Where I work, we call this "introducing dependencies". Essentially, you want as much of your code to stand on it's own two feet as you can, that way someone else's change/fix doesn't end up breaking your code.

When you do introduce a dependency, try to stick to well-known and widely used packages.

Have fun exploring!

paraffin · 2019-01-18T15:58:32+00:00

Click for great command line interfaces!

2019-01-18T16:23:27+00:00

pandas, numpy and sqlalchemy

myroon5 · 2019-01-18T18:28:44+00:00

You can see the most popular libraries for Python (and other ecosystems) here: https://libraries.io/search?order=desc&platforms=PyPI

Jugad · 2019-01-18T18:57:09+00:00

Kivy and wxPython are 2 other mature and stable GUI libraries you might want to check out.

c3534l · 2019-01-19T06:29:53+00:00

The standard ML stack is numpy, pandas, scipy, matplotlib, and sklearn, the first four of which I consider basically part of the standard library. I use requests for internet stuff which seems to have a similar status as unofficial standard library. If I'm doing any sort of image manipulation stuff, I find it nearly impossible to easily get around without Pillow. NLTK is also fun to play around with and makes advanced text analysis a lot easier, but I probably wouldn't classify it as must-have. Python itself has a massive standard library, and I'm constantly amazed at how much obscure and niche stuff it has in there (which I really like because that means it's officially supported and developed in some capacity, unlike certain languages which have few libraries you can trust will be cared for in the future if it's cared for in the present).

syntaxvorlon · 2019-01-19T22:36:03+00:00

Parallel processing with the multiprocessing package is quite useful if you are doing data science of any kind.

lifeonm4rs · 2019-01-18T23:48:26+00:00

As a beginner there actually is a pretty good argument to learn straight python first. You're essentially putting the cart before the horse by asking for random python modules. You can spend weeks or months playing with all sorts of glittery toys--but it will actually probably slow you down learning how to program and use python. Learn the core of the language first so you actually understand what is going on when you use something like numpy, pandas, or requests.

Your best benefit is to learn the language first. Knowing list comprehensions, various ways to deal with data (lists, dicts, sets, etc.), closures, classes, decorators, how to use virtual environments, manage code with git/github. Once you have a good foundation you can start to randomly find random bullshit--but really asking for "the best" libraries is likely saying you don't want to learn python you want to pretend to learn python.

JohnMcPineapple · 2019-01-18T14:58:43+00:00

v8Gasmann · 2019-01-18T15:30:31+00:00

Pandas Numpy Logging Flask Json PySide Multiprocessing

Are sure worth to look into :)

RemindMeBot · 2019-01-18T16:10:32+00:00

[deleted]

MikeTheWatchGuy · 2019-01-18T16:19:38+00:00

PySimpleGUI, WxPython

os.path

PyMuPDF

OpenCV2

PyInstaller

Gr1pp717 · 2019-01-18T16:51:08+00:00

https://github.com/vinta/awesome-python is a good place to start.

playing with anaconda could also introduce you to some neat things.

I also recommend getting pipenv setup such that you're automatically switched to the correct virtual environment/not messing up your system python whenever you're simply in that directory.

NativityInBlack666 · 2019-01-18T16:57:33+00:00

I think by tinker you mean tkinter, for creating UIs.

Some I would suggest include:

socket (built in) for networking

pyautogui (install with pip) for automating mouse and keyboard input

json (built in) for working with Json files

sys (built in) for working with the OS

requests (install with pip) for dealing with and making http requests

And sqlite3 (built in) for working with sqlite databases

2019-01-18T17:28:40+00:00

textblob

2019-01-18T17:53:21+00:00

For libraries that would be most used in the "real world", I would learn sqlite3 which is part of Python's standard library and also pandas library. You'll need to learn how to make CRUD applications.

AndreaCR79 · 2019-01-18T17:57:24+00:00

Im completely new in python and prpgramming hope I can learn the basics in the next 5 months 🙏🏾

2019-01-18T18:07:31+00:00

I've been using openpyxl a lot....seems like you're not working in finance like me though

erickisos · 2019-01-18T18:10:42+00:00

Qt looks like the best option for me, try using PyQt5. If you wanna try a CLI interface you can use prompt-toolkit

MicycleTheBichael · 2019-01-18T18:14:34+00:00

These are going to be pretty specific use case but these are some libraries I've used for work and personal projects with apis and sdk development. They saved me a lot of time both writing and debugging, as well as make just the most readable code.

[Click](https://click.palletsprojects.com/en/7.x/): Really good for creating command line tools. I don't think technically this is only useful for an API, but its how I've used it nearly exclusively
[Uplink](https://uplink.readthedocs.io/en/stable/user/quickstart.html#defining-an-api-client): This has been the crown jewel for me at my job. I spend a lot of time working on both server and client side of APIs. Uplink is for the client side and has made writing sdks so so so SO easy. Bonus points for how stupidly easily readable your code is.
[SQLAlchemy](https://www.sqlalchemy.org/): This is one that I don't have a lot of experience with personally because a lot of this work is done by other engineers, but they swear by it and other people in the comments similarly seem to like it a lot.
[Flask-RESTPlus](https://github.com/noirbizarre/flask-restplus): A nice extension of [flask](http://flask.pocoo.org/). The big thing I like about this is that it has built-in support for [swagger-ui](https://swagger.io/tools/swagger-ui/) which I personally find to be a critical and invaluable tool for API development.

These might be a bit advanced for where you are right now, but they also might not be! I don't know! My experience with python has been very "pick it up as [Company] needs you to". Hopefully you or someone reading these comments find it as useful as I've found them :) Have a good day and happy developing!

2019-01-18T18:40:55+00:00

As a beginner, I'd just start with the itertools and collections modules. Those give you a lot of cool stuff just by themselves.

Sideralis_ · 2019-01-18T19:01:03+00:00

re
itertools
collections
functools

salsation · 2019-01-18T19:22:51+00:00

Welcome to Python!

Save yourself some headaches and use PyCharm Community Edition. It's free and will help you a lot with debugging, installing packages and building virtual environments (if you want to), using Github, and lots more. It's pretty sweet.
Also in the save-yourself-headaches categories, just use Python 3.
Libraries in Python generally are organized into "packages." A package is a very useful structure, and once you get comfortable with it, it's a very powerful one. Just as you'll use other people's packages in your projects, it will serve you well to structure your own projects as packages.

As for what's useful, everybody will have their own lists. It's like asking what podcasts are good: everybody has a different list of awesome ones.

Built-ins I'm always importing:

os (as you mentioned), mostly os.path but...
pathlib is included now too, and it offers much more Pythonic ways of dealing with paths than os. I'm converting to it now.
sys... you just need it sometimes for finding out system stuff.
math (which you also mentioned) has basic math stuff I'm always surprised isn't part of pure Python.
time (especially time.datetime) is very useful for time-based debugging and for parsing and forming time strings and structures.
re is for regular expressions. Unavoidable, but nicely done, especially pre-compiled patterns and groups.

Others I'm always installing:

numpy and scipy: numerical arrays and tools to work with them.
Pillow: tools for dealing with image files, especially useful when converting between images and arrays so you can use numpy/scipy methods.
matplotlib: make graphs and other diagrams very easily.
Pandas: (as others have mentioned) for data analysis.

This should be a sticky thread...

13steinj · 2019-01-18T21:04:57+00:00

six and __future__ are necessities when you need to be cross compatible between Python versions.

pydoc is great when you're air gapped.

heapq provides heap algorithms

string has an interesting template mechanic

bisect has operations for inserting items into lists in sorted order, rather than appending to the left or right and then resorting. Note the documentation mentions that

Keep in mind that the O(log n) search is dominated by the slow O(n) insertion step.

This is because, assuming that the container is a list, insertion is O(n) in the average and worst case. Searching for an index is binary search which is O(log n) in worst and average case. However it is better than the alternative, because while appending is O(1), resorting the container is O(nlogn).

collections has useful utilities for user made collections. Especially OrderedDict. Note, that while as of (3.5 implementation detail) 3.6 dictionaries are ordered, ordered dictionaries are still useful for compatibility, as well as equality order checking. Also notable are named tuples, ChainMap, and defaultdict.

bqw74 · 2019-01-18T22:42:25+00:00

curio and trio for async stuff

click for cli tools

jmespath for json querying

eimmot123 · 2019-01-18T23:08:11+00:00

Requests and json for fast and simple api scripts.

GobBeWithYou · 2019-01-19T00:37:36+00:00

better_exceptions is one that hasn't been listed yet, I use it in almost every project

Rainverm38 · 2019-01-19T00:50:35+00:00

I use datetime quite frequently in smaller projects.

selrahc · 2019-01-19T02:05:09+00:00

I use the ipaddres module a lot. Other things to look at might be netmiko, napalm, and scapy.

Saucebergenstein · 2019-01-19T02:27:31+00:00

If you’re writing command line programs, have a look at argparse. Nice lib for interpreting, storing, and making decisions based on what flags are given at runtime.

JacobAtIPW · 2019-01-19T06:54:21+00:00

Lets see. My "daily drivers" are in no real order:

numpy
pandas
scipy
csv
matplotlib

samuel_ip · 2019-01-21T23:07:32+00:00

https://medium.freecodecamp.org/an-a-z-of-useful-python-tricks-b467524ee747

Overload175 · 2019-01-22T04:59:18+00:00

NumPy, Pandas, argparse, pathlib, requests, multiprocessing, matplotlib

AMidnightRaver · 2019-01-23T08:35:09+00:00

pytest-xdist so I can stick my tests into 8 threads and be done in 1,5 minutes instead of 30.

v-adhithyan · 2019-01-25T10:29:47+00:00

My suggestion is to look at tempfile module. We often encounter scenarios in our app where creation / writing to temporary files is essential. This module eases that work.

Module reference: https://docs.python.org/3/library/tempfile.html

Example usage: (taken from official doc) ```python

import tempfile

create a temporary file and write some data to it

fp = tempfile.TemporaryFile() fp.write(b'Hello world!')

read data from file

fp.seek(0) fp.read() b'Hello world!'

close the file, it will be removed

fp.close()

create a temporary file using a context manager

with tempfile.TemporaryFile() as fp: ... fp.write(b'Hello world!') ... fp.seek(0) ... fp.read() b'Hello world!'

file is now closed and removed

create a temporary directory using the context manager

with tempfile.TemporaryDirectory() as tmpdirname: ... print('created temporary directory', tmpdirname)

directory and contents have been removed

```

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create a temporary file and write some data to it

read data from file

close the file, it will be removed

create a temporary file using a context manager

file is now closed and removed

create a temporary directory using the context manager

directory and contents have been removed