How do I save R output into a file in google colab? It could be saved on google drive or my local drive, either would work.
For example, if I wanted to save a list of R objects in a RDS file, normally I would have used something like this on RStudio.
saveRDS(list(a, b, c, d), file = "C:\\sim1.rds")
I am looking to do something similar on Google colab.
Recently I found the answer so I wanted to write it here in case it is useful for others.
To save an output on my google drive we need to mount it using the following.
from google.colab import drive
drive.mount('/content/drive')
Then we can navigate to MyDrive using the following.
cd /content/drive/MyDrive
Now that we are in MyDrive, we can run the code and save outputs on MyDrive. Then we can download it to our laptop.
Related
For loading the data I am using the following code for the data that is located in google drive.
import glob
dataAD = glob.glob('ADNI_komplett/AD/*.nii.gz')
dataLMCI = glob.glob('ADNI_komplett/LMCI/*.nii.gz')
dataCN = glob.glob('ADNI_komplett/CN/*.nii.gz')
dataFiles = dataAD + dataLMCI + dataCN
I need to access the same data in jupyter notebook in my local machine for which I am downloading the data from google drive to my machine and trying to load the files using the same code as above.
But I noticed that the order in which the files getting loaded is different in colab vs jupyter.
Adding screen shots to show difference
Check screenshots.
On the left side of the screenshot, is the code run in my machine in jupyter and on the right side, is the code run in colab.
As you can notice from the highlighted region; in jupyter the 1st file name loaded is AD\mwp1ADNI_002_S_0729_MR_MT1 whereas in colab the 1st file loaded is AD/mwp1AD_4001_037_MR_MT1 and also from the second screenshot it can be seen that the number ordering is also different.
I need to maintain the ordering in both colab and jupyter.
Any suggestion for this problem is appreciated.
glob returns files in the order they appear within the filesystem (see How is Pythons glob.glob ordered?). Colaboratory is running on a different filesystem architecture than your local Jupyter runtime, and so it's not surprising that the orders are different.
If you require files to be listed in the same order cross-platform, I'd suggest sorting the outputs in Python; i.e.
dataAD = sorted(glob.glob('ADNI_komplett/AD/*.nii.gz'))
I have a csv on my computer that I can upload to Google Drive. I am trying to use a Google colab but in R and not Python. How can I import this csv?
https://stackoverflow.com/a/57927212/5333248
Here a workaround for you.
Lateral arrow on top-left of the screen >> files >> upload.
In this way you can upload the .csv file from your pc.
There's even a Mount Drive option in the same path, but as I understand is only for python.
The file last only for the current session. You'll need to re-upload it every time you reopen the notebook on Google Colab!
Just to clarify which is the lateral arrow
How to load CSV files in google colab for R?
For python, there are many answers but can someone guide how file can be imported in R for google colab.
Assuming you mean "get a CSV file from my local system into the Colaboratory Environment" and not just importing it from inside the Colab file paths as per Korakot's suggestion, since your question wasn't very clear, I think you have two main options:
1. Upload a file directly through the shortcut in the side menu thingy.
Just click the icon there and upload your file to drive. Then, you can run normal r import functions by following the internal path like korakot put in this answer.
2. Connect your google drive
Assuming you're using a notebook like the one created by Thong Nguyen, you can use a python call to mount your own google drive, like this one:
cat(system('python3 -c "from google.colab import drive\ndrive.mount()"', intern=TRUE), sep='\n', wait=TRUE)
... which will initiate the login process to Google Drive and will allow you to access your files from google drive as if they were folders in colab. There's more info about this process here.
In case you use the Colab with R as runtime type (and Python code would not work therefore), you could also simply upload the file as MAIAkoVSky suggested in step 1 and then import it with
data <- read.csv('/content/your-file-name-here.csv')
The filepath can also be accessed by right clicking on the file in the interface.
Please be aware that the files will disappear once you disconnected from Colab. You would need to upload them again for the next session.
You can call the read.csv function like
data = read.csv('sample_data/mnist_test.csv')
We're using Google Cloud Dataproc for quick data analysis, and we use Jupyter notebooks a lot. A common case for us is to generate a report which we then want to download as a csv.
In a local Jupyter env this is possible using FileLink for example:
from IPython.display import FileLinks
df.to_csv(path)
FileLinks(path)
This doesn't work with Dataproc because the notebooks are kept on a Google Storage bucket and the links generated are relative to that prefix, for example http://my-cluster-m:8123/notebooks/my-notebooks-bucket/notebooks/my_csv.csv
Does anyone know how to overcome this? Of course we can scp the file from the machine but we're looking for something more convenient.
To share report you can save it to Google Cloud Storage (GCS) instead of local file.
To do so, you need to convert your Pandas DataFrame to Spark DataFrame and write it to GCS:
sparkDf = SQLContext(SparkContext.getOrCreate()).createDataFrame(df)
sparkDf.write.csv("gs://<BUCKET>/<path>")
I am using Google Colaboratory & github.
I create a new Google Colab notebook, and I clone my github project into it using a simple !git clone <github_link> in the notebook.
Now, I have a Jupyter notebook in my github project that I need to run on Google Colab. How do I do that?
There is not a real need of downloading the notebook. If you already have your Notebook in a GitHub repo, the only thing you need to do is:
Open your Notebook file on GitHub in any browser (So the URL ends in .ipynb).
Change the URL from https://github/full_path_to_your_notebook to https://colab.research.google.com/github/full_path_to_your_notebook
And that should work.
You can upload the notebook to google drive first, then open it from there.
go to drive.google.com
go into directory “Colab Notebooks”
choose “New” > File upload
After uploading, click the new file
Chose “Open with Colaboratory” at the top
The two most practical ways are both through the Google Drive webinterface.
The first method is what #Korakot Choavavanich described.
The advantage of this method is that it provides a Search window to search for your file in your google drive storage.
The second method is even more convenient - and maybe more appropriate for your case:
In the Google Drive webinterface, you navigate to your folder where your file is located - in your case within the cloned github repository.
Then (see screenshot):
right-click on the file | Open with | Colaboratory
Your file is then converted into a colabo notebook automatically (it takes at least half a minute for that).
The advantage with this method is that you can create the colabo file directly in the folder.
My tip is to create a copy of the original jupyter file (I added "COLABO" in the file name) as you will have different code to sync your google drive and save files than in a local jupyter notebook.
One of the way could be that you can connect your google drive with the Colaboraty notebook using the following link:
Link to images within google drive from a colab notebook
Post which you can download your github repo in your google drive location. Then browse through your google drive and open the notebook using Colaboratory itself.
import sys, os
sys.path.append('models/research')
sys.path.append('models/research/object_detection')
It helped me. I was also looking for it, and found it in this COLAB work
https://colab.research.google.com/drive/1EQ3Lt_ez-oKTtVMebh6Tm3XSyPPOHAf3#scrollTo=oC-_mxCxCNP6
The better option I have found is copying the code from each cell and executing the code in colab, if you clone the Github and containing ipynb file in that. By doing this you won't face any difficulties.
Upload the .ipynb file directly in colab. Just go to colab, in the tabs above there should be upload. choose the file and upload there.
It may be a new feature not mentioned in other answers.
But right now Colab allows running jupyter notebooks directly from github, even from private repos.
Login to your google account
Access colab.research.google.com
Select the GitHub tab.
Choose include private repository if needed.
Go through the authentication process in the new opened window
Select from your repos and notebooks
And clone your repo from inside the opened notebook.