Python Download Multiple Zip Files From Url High Quality
Rachele Weishaar
At the moment the different concerns of a Python application may not be obvious to you. That's normal! Over time, you'll learn more about this. With more experience, it'll become clearer when things will benefit from getting split into files.
Splitting your code into multiple files is a great way to produce smaller, more focused files. Navigating theses smaller files will be easier and so will understanding the content of each of these files.
I have a code that I wish to split apart into multiple files. In matlab one can simply call a .m file, and as long as it is not defined as anything in particular it will just run as if it were part of the called code. Example (edited):
test.m (matlab)
Now, this file ("test.py") is in python terminology a "module". We can import it (as long as it can be found in our PYTHONPATH) Note that the current directory is always in PYTHONPATH, so if use_test is being run from the same directory where test.py lives, you're all set:
There's a lot more you can do than just that through the use of special __init__.py files which allow you to treat multiple files as a single module), but this answers your question and I suppose we'll leave the rest for another time.
I am researching module usage in python just now and thought I would answer the question Markus asks in the comments above ("How to import variables when they are embedded in modules?") from two perspectives:
(Historical note: In earlier versions of Python, you can sometimes usecontextlib.nested() to nest context managers. This won't work as expected for opening multiples files, though -- see the linked documentation for details.)
For opening many files at once or for long file paths, it may be useful to break things up over multiple lines. From the Python Style Guide as suggested by @Sven Marnach in comments to another answer:
How you create one on your own machine depends on what operating system you use; once you have one, you can upload it on the "Files" page, and then use the unzip command from bash to extract from zip files, or the unrar one to extract from rar files.
If you're using them to download, you can use the corresponding zip and rar commands on bash to create compressed files, then you can download them from the "Files" tab and use whatever tools you have on your local machine to extract the contents.
Although logging is thread-safe, and logging to a single file from multiplethreads in a single process is supported, logging to a single file frommultiple processes is not supported, because there is no standard way toserialize access to a single file across multiple processes in Python. If youneed to log to a single file from multiple processes, one way of doing this isto have all the processes log to a SocketHandler, and have aseparate process which implements a socket server which reads from the socketand logs to file. (If you prefer, you can dedicate one thread in one of theexisting processes to perform this function.)This section documents this approach in more detail andincludes a working socket receiver which can be used as a starting point for youto adapt in your own applications.
When deploying Web applications using Gunicorn or uWSGI (or similar), multiple workerprocesses are created to handle client requests. In such environments, avoid creatingfile-based handlers directly in your web application. Instead, use aSocketHandler to log from the web application to a listener in a separateprocess. This can be set up using a process management tool such as Supervisor - seeRunning a logging socket listener in production for more details.
To analyze multiple files, we will need to import a python library. A library is a set of modules which contain functions. The functions within a library or module are usually related to one another. Using libraries and in Python reduces the amount of code you have to write. In the last lesson, we imported os.path, which was a module that handled filepaths for us.
In this lesson, we will be using the glob library, which will help us read in multiple files from our computer. Within a library there are modules and functions which do a specific computational task. Usually a function has some type of input and gives a particular output. To use a function that is in a library, you often use the dot notation introduced in the previous lesson. In general
We are going to import two libraries. One is the os library which controls functions related to the operating system of your computer. We used this library in the last lesson to handle filepaths. The other is the glob library which contains functions to help us analyze multiple files. If we are going to analyze multiple files, we first need to specify where those files are located.
Python can only write strings to files. Our current print statement is not a string; it prints two python variables. To convert what we have now to a string, you place a capital F in front of the line you want to print and enclose it in single quotes. Each python variable is placed in braces. Then you can either print the line (as we have done before) or you can use the filehandle.write() command to print it to a file.
I'm working on an addon that uses an ExportHelper class to implement a file import dialog. I want the user to be able to select multiple files, but the filepath variable that is populated is just a string with one filename in it.
Apache Arrow is an ideal in-memory transport layer for data that is being reador written with Parquet files. We have been concurrently developing the C++implementation ofApache Parquet,which includes a native, multithreaded C++ adapter to and from in-memory Arrowdata. PyArrow includes Python bindings to this code, which thus enables readingand writing Parquet files with pandas as well.
Note: the partition columns in the original table will have their typesconverted to Arrow dictionary types (pandas categorical) on load. Ordering ofpartition columns is not preserved through the save/load process. If readingfrom a remote filesystem into a pandas dataframe you may need to runsort_index to maintain row ordering (as long as the preserve_indexoption was enabled on write).
Currently, HDFS andAmazon S3-compatible storage aresupported. See the Filesystem Interface docs for more details. For thosebuilt-in filesystems, the filesystem can also be inferred from the file path,if specified as a URI:
In previous versions, there was no support for the allow_multiple_selectedclass attribute, and users were advised to create the widget with the HTMLattribute multiple set through the attrs argument. However, thiscaused validation of the form field to be applied only to the last filesubmitted, which could have adverse security implications.
Thanks for the suggestion but I am not sure if that helps in my case. As far as I understand does your code process the files in parallel. That is nice but cannot be used if you want information from all files, e.g. a simple mean over time (cdo timmean). Considering the given example I would like to compute a monthly climatology (cdo ymonmean). Can multi processing help in this case?
When it comes to file retrieval, Python offers a robust set of tools and packages that are useful in a variety of applications, from web scraping to automating scripts and analyzing retrieved data. Downloading files from a URL programmatically is a useful skill to learn for various programming and data projects and workflows.
Using Python also offers the possibility of automating your processes, saving you time and effort. Some examples include automating retries if a download fails, retrieving and saving multiple files from URLs, and processing and storing your data in designated locations.
Downloading multiple files is yet another common scenario in Python. In such a case, you can speed up the download process by getting the files in parallel. There are two popular approaches to downloading several files simultaneously:
In addition to multithreading, another method to download multiple files concurrently is by using the async/await pattern in Python. It involves running multiple non-blocking tasks asynchronously in an event loop by allowing them to suspend and resume execution voluntarily as a form of cooperative multitasking. This is different from threads, which require a preemptive scheduler to manage context switching between them.
Asynchronous tasks also differ from multithreading in that they execute concurrently within a single thread instead of multiple threads. Therefore, they must periodically give up their execution time to other tasks without hogging the CPU. By doing so, I/O-bound tasks such as asynchronous downloads allow for concurrency, as the program can switch between tasks and make progress on each in parallel.
The next step is defining an asynchronous function to download a file from a URL. You can do so by creating an aiohttp.ClientSession instance, which holds a connector reused for multiple connections. It automatically keeps them alive for a certain time period to reuse them in subsequent requests to the same server whenever possible. This improves performance and reduces the overhead of establishing new connections for each request.
You run the main() function using asyncio.run(), which initiates and executes the asynchronous tasks in an event loop. This code downloads files concurrently from the specified URLs without blocking the execution of other tasks.
When downloading multiple files, you can either use the requests library together with multithreading or the aiohttp library with asynchronous downloads to perform concurrent downloads. When used properly, either can improve performance and optimize the download process.
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