What are the common ways to import private data into Google Colaboratory notebooks? Is it possible to import a non-public Google sheet? You can't read from system files. The introductory docs link to a guide on using BigQuery, but that seems a bit... much.
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google-sheets
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On the left bar of any colaboratory there is a section called "Files". Upload your files there and use this path
"/content/YourFileName.extension"
ex: pd.read_csv('/content/Forbes2015.csv');
You can mount to google drive by running following
from google.colab import drive
drive.mount('/content/drive')
Afterwards For training copy data from gdrive to colab root folder.
!cp -r '/content/drive/My Drive/Project_data' '/content'
where first path is gdrive path and second is colab root folder.
This way training is faster for large data.
You can also use my implementations on google.colab and PyDrive at https://github.com/ruelj2/Google_drive which makes it a lot easier.
!pip install - U - q PyDrive
import os
os.chdir('/content/')
!git clone https://github.com/ruelj2/Google_drive.git
from Google_drive.handle import Google_drive
Gd = Google_drive()
Then, if you want to load all files in a Google Drive directory, just
Gd.load_all(local_dir, drive_dir_ID, force=False)
Or just a specific file with
Gd.load_file(local_dir, file_ID)
Upload
from google.colab import files
files.upload()
Download
files.download('filename')
List directory
files.os.listdir()
Simple way to import data from your googledrive - doing this save people time (don't know why google just doesn't list this step by step explicitly).
INSTALL AND AUTHENTICATE PYDRIVE
!pip install -U -q PyDrive ## you will have install for every colab session
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
UPLOADING
if you need to upload data from local drive:
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(name=fn, length=len(uploaded[fn])))
execute and this will display a choose file button - find your upload file - click open
After uploading, it will display:
sample_file.json(text/plain) - 11733 bytes, last modified: x/xx/2018 - %100 done
User uploaded file "sample_file.json" with length 11733 bytes
CREATE FILE FOR NOTEBOOK
If your data file is already in your gdrive, you can skip to this step.
Now it is in your google drive. Find the file in your google drive and right click. Click get 'shareable link.' You will get a window with:
https://drive.google.com/open?id=29PGh8XCts3mlMP6zRphvnIcbv27boawn
Copy - '29PGh8XCts3mlMP6zRphvnIcbv27boawn' - that is the file ID.
In your notebook:
json_import = drive.CreateFile({'id':'29PGh8XCts3mlMP6zRphvnIcbv27boawn'})
json_import.GetContentFile('sample.json') - 'sample.json' is the file name that will be accessible in the notebook.
IMPORT DATA INTO NOTEBOOK
To import the data you uploaded into the notebook (a json file in this example - how you load will depend on file/data type - .txt,.csv etc. ):
sample_uploaded_data = json.load(open('sample.json'))
Now you can print to see the data is there:
print(sample_uploaded_data)
The Best and easy way to upload data / import data into Google colab GUI way is click on left most 3rd option File menu icon and there you will get upload browser files as you get in windows OS .Check below the images for better easy understanding.After clicking on below two options you will get upload window box easy. work done.
from google.colab import files
files=files.upload()
As mentioned by @Vivek Solanki, I also uploaded my file on the colaboratory dashboard under "File" section.
Just take a note of where the file has been uploaded. For me,
train_data = pd.read_csv('/fileName.csv')
worked.
If the Data-set size is less the 25mb, The easiest way to upload a CSV file is from your GitHub repository.
Example:
import pandas as pd
url = 'copied_raw_data_link'
df1 = pd.read_csv(url)
df1.head()
if you want to do this without code it's pretty easy. Zip your folder in my case it is
dataset.zip
then in Colab right click on the folder where you want to put this file and press Upload and upload this zip file. After that write this Linux command.
!unzip <your_zip_file_name>
you can see your data is uploaded successfully.
I created a small chunk of code that can do this in multiple ways. You can
import os.path
filename = "your_file_name.csv"
if os.path.isfile(filename):
print("File already exists. Will reuse the same ...")
else:
use_github_data = False # Set this to True if you want to download from Github
if use_github_data:
print("Loading fie from Github ...")
# Change the link below to the file on the repo
filename = "https://github.com/ngupta23/repo_name/blob/master/your_file_name.csv"
else:
print("Please upload your file to Colab ...")
from google.colab import files
uploaded = files.upload()
Here is one way to import files from google drive to notebooks.
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
from google.colab import auth
auth.authenticate_user()
from oauth2client.client import GoogleCredentials
creds = GoogleCredentials.get_application_default()
import getpass
!google-drive-ocamlfuse -headless -id={creds.client_id} -secret= {creds.client_secret} < /dev/null 2>&1 | grep URL
vcode = getpass.getpass()
!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}
!mkdir -p drive
!google-drive-ocamlfuse drive
lets say your dataset file in Colab_Notebooks folder and its name is db.csv
import pandas as pd
dataset=pd.read_csv("drive/Colab_Notebooks/db.csv")
I hope it helps
This allows you to upload your files through Google Drive.
Run the below code (found this somewhere previously but I can't find the source again - credits to whoever wrote it!):
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
from google.colab import auth
auth.authenticate_user()
from oauth2client.client import GoogleCredentials
creds = GoogleCredentials.get_application_default()
import getpass
!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL
vcode = getpass.getpass()
!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}
Click on the first link that comes up which will prompt you to sign in to Google; after that another will appear which will ask for permission to access to your Google Drive.
Then, run this which creates a directory named 'drive', and links your Google Drive to it:
!mkdir -p drive
!google-drive-ocamlfuse drive
If you do a !ls
now, there will be a directory drive, and if you do a !ls drive
you can see all the contents of your Google Drive.
So for example, if I save my file called abc.txt
in a folder called ColabNotebooks
in my Google Drive, I can now access it via a path drive/ColabNotebooks/abc.txt
step 1- Mount your Google Drive to Collaboratory
from google.colab import drive
drive.mount('/content/gdrive')
step 2- Now you will see your Google Drive files in the left pane (file explorer). Right click on the file that you need to import and select çopy path. Then import as usual in pandas, using this copied path.
import pandas as pd
df=pd.read_csv('gdrive/My Drive/data.csv')
Done!
It has been solved, find details here and please use the function below: https://stackoverflow.com/questions/47212852/how-to-import-and-read-a-shelve-or-numpy-file-in-google-colaboratory/49467113#49467113
from google.colab import files
import zipfile, io, os
def read_dir_file(case_f):
# author: yasser mustafa, 21 March 2018
# case_f = 0 for uploading one File and case_f = 1 for uploading one Zipped Directory
uploaded = files.upload() # to upload a Full Directory, please Zip it first (use WinZip)
for fn in uploaded.keys():
name = fn #.encode('utf-8')
#print('\nfile after encode', name)
#name = io.BytesIO(uploaded[name])
if case_f == 0: # case of uploading 'One File only'
print('\n file name: ', name)
return name
else: # case of uploading a directory and its subdirectories and files
zfile = zipfile.ZipFile(name, 'r') # unzip the directory
zfile.extractall()
for d in zfile.namelist(): # d = directory
print('\n main directory name: ', d)
return d
print('Done!')
in google colabs if this is your first time,
from google.colab import drive
drive.mount('/content/drive')
run these codes and go through the outputlink then past the pass-prase to the box
when you copy you can copy as follows, go to file right click and copy the path ***don't forget to remove " /content "
f = open("drive/My Drive/RES/dimeric_force_field/Test/python_read/cropped.pdb", "r")
For those who, like me, came from Google for the keyword "upload file colab":
from google.colab import files
uploaded = files.upload()
Quick and easy import from Dropbox:
!pip install dropbox
import dropbox
access_token = 'YOUR_ACCESS_TOKEN_HERE' # https://www.dropbox.com/developers/apps
dbx = dropbox.Dropbox(access_token)
# response = dbx.files_list_folder("")
metadata, res = dbx.files_download('/dataframe.pickle2')
with open('dataframe.pickle2', "wb") as f:
f.write(res.content)
The simplest solution I have found so far which works perfectly for small to mid-size CSV files is:
pandas.read_csv(URL)
This may or may not work for reading a text file line by line or binary files.
The simplest way I've made is :
Source: Stackoverflow.com