I use following codes to save some data frame data, in Google Colab. But it is saved in the "local file system", not in my computer nor Google Drive. How can I get the Excel file from there?
Thanks!
writer = pd.ExcelWriter('hey.xlsx')
df.to_excel(writer)
writer.save()
from google.colab import files
files.download('result.csv')
Use Google Chrome. Firefox shows Network Error.
You'll want to upload the file using something like:
from google.colab import files
uploaded = files.upload()
Then, you can pick out the file data using something like uploaded['your_file_here.xls'].
There's a distinct question that includes recipes for working with Excel files in Drive that might be useful:
https://stackoverflow.com/a/47440841/8841057
Related
I have a function with two variables called check(new_doc old_doc) which is written on a .py file called checking.py that I stored on google drive.
I would like to call that function in a new google Colab notebook but when I do so I get a ValueError: I/O operation on closed file.
The function uses the with open method twice to read and check the documents given as a input, maybe this info can help solve the issue.
This is what I tried
from google.colab import drive drive.mount('/content/gdrive') import sys sys.path.insert(0,'/content/gdrive/MyDrive/check folder') from checking import check check(new_doc old_doc)
then I get the ValueError: I/O operation on closed file. Error
I'm trying to download the wmt14_translate/fr-en dataset from TensorFlow-datasets in Google Colab under the free tier. Downloading the dataset itself is taking me over 12 hours. Is there any alternative using Google Drive or something since I already have the data stored on my laptop.
PS - The file format of the dataset isn't really clear since it does not even end with a '.'.
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Upload the dataset to google drive.
Then, in colab,
from google.colab import drive
drive.mount('/content/drive')
You can use wget to download datasets. Downloading by wget utility is much faster than uploading.
Also, if you'll use kaggle dataset someday you can use kaggle datasets download.
I started recently solving Kaggle competitions, using 2 computers (laptop and PC). Kaggle gives big amout of data for training ML.
The biggest problem for me is downloading that data, it takes about 30 GB, and bigger issue, unzipping it. I was working on my laptop, but I decided to move to PC. I saved the ipynb file and closed laptop.
After opening this file I saw that all unzipped data went missing and I need to spend 2h for downloading and unzipping it once again.
Is it possible to save all unzipped data with this notebook? Or maybe it's stored somewhere on Google Disk?
You can leverage the storage capacity of GoogleDrive. Colab allows you to have this data stored on your Drive and access it from colab notbook as follows:
from google.colab import drive
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import pandas as pd
drive.mount('/content/gdrive')
img = mpimg.imread(r'/content/gdrive/My Drive/top.bmp') # Reading image files
df = pd.read_csv('/content/gdrive/My Drive/myData.csv') # Loading CSV
When it mounts, it would ask you to visit a particular url to grant permission for accessing drive. Just paste the token returned. Needs to be done only once.
The best thing about colab is you can also run shell cmds from code, all you need to do is to prefix the commands with a ! (bang). Useful when you need to unzip etc.
import os
os.chdir('gdrive/My Drive/data') #change dir
!ls
!unzip -q iris_data.zip
df3 = pd.read_csv('/content/gdrive/My Drive/data/iris_data.csv')
Note: Since you have specified that the data is about 30GB, this may not be useful if you are on the free tier provided by Google (as it gives only 15GB per account) you may have to look elsewhere.
You can also visit this particular question for more solutions on Kaggle integration with Google Colab.
I've been working with a dataframe in google colab, and i convert it to a json format using df.to_json(), now i stuck on how to download it into my local disk (or google drive). i found the answers but in csv format, not json.
Any help is appreciated - thanks!
You can proceed in steps: 1) save the output of to_json() to a file on the Colab backend. Then, use the built-in download helper
from google.colab import files
files.download('colab_file_name')
Here's a complete example that starts with a randomly generated DataFrame:
https://colab.research.google.com/drive/1K95vU0gUJW4iJ4FaHxZ1HSQguOP1flEg
Was there a way to write out files with google colab?
For example, if I use
import requests
r = requests.get(url)
Where will those files be stored? Can they be found?
And similarly, can I get the file I outputted via say tensorflow save function
saver=tf.Saver(....)
...
path = saver.save(sess, "./my_model.ckpt")
Thanks!
In your first example, the data is still in r.content. So you also need to save them first with open('data.dat', 'wb').write(r.content)
Then you can download them with files.download
from google.colab import files
files.download('data.dat')
Downloading your model is the same:
files.download('my_model.ckpt')
I found it is easier to first mount your Google drive to the non-persistent VM and then use os.chdir() to change your current working folder.
After doing this, you can do the exactly same stuff as in local machine.
I have a gist listing several ways to save and transfer files between Colab VM and Google drive, but I think mounting Google drive is the easiest approach.
For more details, please refer to mount_your_google_drive.md in this gist
https://gist.github.com/Joshua1989/dc7e60aa487430ea704a8cb3f2c5d6a6