How to Save and Upload Files to Google Colab
Importing Data to Google Colab — the Make clean Way
Because clean lawmaking is of import!
In this article I volition present:
- An introduction of Google Colab
- 2 much-used "quick and dirty" methods to upload data to Colab
- 2 automatic, "clean" methods to upload data to Colab
What is Google Colab?
Information technology is still hard to believe, but it is true. Nosotros tin can run heavy information science notebooks for free on Google Colab.
Colab is a Cloud service, which means that a server at Google will run the notebook rather than your ain, local computer.
Mayhap even more surprising is that the hardware backside it is quite practiced!
Is Colab the perfect new notebook solution?
At that place is ane big issue with Google Colab, often discussed earlier, which is the storage of your data. Notebooks, for example, Jupyter notebooks, often utilise data files stored locally, on your computer. This is often done using a uncomplicated read_csv statement or comparable.
The Deject's local is non your local.
Simply Google Colaboratory is running in the Cloud. The Cloud'due south local is not your local. Therefore a read_csv statement will search for the file on Google's side rather than on your side. And so it volition not detect it.
How to go your information into Colab — the manual manner?
To get your data into your Colab notebook, I first discuss the two almost known methods, together with their advantages and disadvantages. After that, I discuss two alternative solutions, that can be more appropriate especially when your code has to exist like shooting fish in a barrel to industrialize.
Transmission Method 1 — using files.upload() to upload information to Colab
- Using files.upload() directly in the Colab notebook gives yous a traditional upload push button that allows yous to move files from your computer into to the Colab surroundings.
2. And so you utilize io.StringIO() together with pd.read_csv to read the uploaded file into a data frame
Advantage of using files.upload() to upload data to Colab:
This is the easiest approach of all, even though it requires a few lines of code.
Disadvantages of using files.upload() to upload data to Colab:
For big files, the upload might take a while. And and so whenever the notebook is restarted (for case if it fails or other reasons…), the upload has to exist redone manually. This is not the best solution, because firstly our code wouldn't re-execute automatically when relaunched and secondly it requires tedious manual operations in example of notebook failures.
Transmission Method 2 — Mounting your Google Drive onto Colab
Upload your data to Google Drive before getting started with the notebook. And then you mountain your Google Drive onto the Colab environment: this means that the Colab notebook can now admission files in your Google Drive.
- Mountain your bulldoze using drive.mount()
ii. Access anything in your Google Drive directly
Advantages of mounting your Google Drive onto Colab:
This is also quite like shooting fish in a barrel. Google Drive is very user-friendly and uploading your information to Google Drive is no problem for most people. As well, once the upload is done, it does not require manual reloading when restarting the notebook. Then it's improve than approach one.
Disadvantages of mounting your Google Drive onto Colab:
The main disadvantage I run across from this approach is mainly for company / industrial use. Every bit long as y'all're working on relatively small projects, this arroyo is great. Merely if access management and security are at stake, you will discover that this approach is difficult to industrialize.
Also, you may not want to be in a 100% Google Environs, as multi-deject solutions give yous more independence from different Cloud vendors.
The Make clean Manner — use External Information Stores
If your project is small, and if you know that information technology will always remain only a notebook, previous approaches tin be acceptable. But for any project that may grow larger in the future, separating information storage from your notebook is a good stride towards a ameliorate architecture.
If you want to move towards a cleaner architecture for data storage in your Google Colab notebook, try going for a proper Data Storage solution.
There are many possibilities in Python to connect with information stores. I here propose ii solutions: AWS S3 for file storage and SQL for relational database storage:
Make clean method one — connect an AWS S3 saucepan
S3 is AWS's file storage, which has the advantage of existence very similar to the previously described ways of inputting data to Google Colab. If you lot are not familiar with AWS S3, don't hesitate to have a wait over hither.
Accessing S3 file storage from Python is very clean lawmaking and very performant. Adding authentification is possible.
Advantages of using S3 with Colab:
S3 is taken seriously equally a data storage solution by the software community, while Google Drive, though more than appreciated for private users, is preferred past many developers only for the integration with other Google Services.
This approach, therefore, improves both your code and your architecture!
Disadvantages of using S3 with Colab:
To apply this method, y'all will need to apply AWS. It is easy, merely it may still be a disadvantage in some cases (e.g. company policy). Also, it may take fourth dimension to load the data every fourth dimension. Information technology can be longer than loading from Google Bulldoze since the information source is carve up.
Clean Method 2 — connect an SQL Database to Colab
If you have data already in a relational database like MySQL or other, it would as well be a skillful solution to plug your Colab notebook straight to your database.
SQLAlchemy is a package that allows you to ship SQL queries to your relational database and this will allow to have well-organized data in this separate SQL environment while keeping but your Python operations in your Colab notebook.
Advantages of connecting an SQL Database to Colab:
This is a good idea when you are starting to get to more serious applications and you lot want to have already a proficient information storage during your development.
Disadvantages of connecting an SQL Database to Colab:
It will be impossible to utilize Relational Information Storage with unstructured data, simply a nonrelational database may exist the answer in this case. A more serious problem can be the query execution time in case of very large volumes. It tin likewise exist a brunt to manage the database (if you don't have one or if you cannot easily share access).
Conclusion
Google Colab notebooks are great but it tin be a existent struggle to get information in and out.
Google Colab notebooks are corking only information technology can be a real struggle to get data in and out.
Importing data by Manual Upload or Mounting Google Drive are both easy to utilise simply difficult to industrialize. Alternatives like AWS S3 or a Relational database will make your system less manual and therefore improve.
The two manual methods are cracking for small short-term projects and the two methods with external storage should be used when a project needs a make clean data shop.
Think through your compages before it'south also late!
Each method has its advantages and disadvantages and only you lot tin determine which 1 fits with your use case. Whatsoever storage you use, but exist certain to think through your compages earlier it's too late!
I hope this article will aid you with edifice your projects. Stay tuned for more than and thanks for reading!
Source: https://towardsdatascience.com/importing-data-to-google-colab-the-clean-way-5ceef9e9e3c8
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