How to Upload a File to Colab
Get Started: 3 Ways to Load CSV files into Colab
Data science is nada without data. Yep, that'due south obvious. What is not so obvious is the series of steps involved in getting the data into a format which allows you to explore the information. Y'all may be in possession of a dataset in CSV format (short for comma-separated values) but no idea what to do next. This post will help you lot go started in data science by allowing you to load your CSV file into Colab.
Colab (short for Colaboratory) is a free platform from Google that allows users to lawmaking in Python. Colab is substantially the Google Suite version of a Jupyter Notebook. Some of the advantages of Colab over Jupyter include an easier installation of packages and sharing of documents. Yet, when loading files like CSV files, it requires some actress coding. I will evidence you iii ways to load a CSV file into Colab and insert information technology into a Pandas dataframe.
(Note: at that place are Python packages that behave mutual datasets in them. I will not discuss loading those datasets in this commodity.)
To starting time, log into your Google Account and go to Google Drive. Click on the New button on the left and select Colaboratory if information technology is installed (if not click on Connect more apps, search for Colaboratory and install it). From in that location, import Pandas equally shown beneath (Colab has it installed already).
import pandas as pd 1) From Github (Files < 25MB)
The easiest mode to upload a CSV file is from your GitHub repository. Click on the dataset in your repository, then click on View Raw. Copy the link to the raw dataset and store it equally a string variable called url in Colab as shown below (a cleaner method simply it's not necessary). The last footstep is to load the url into Pandas read_csv to get the dataframe.
url = 'copied_raw_GH_link' df1 = pd.read_csv(url) # Dataset is now stored in a Pandas Dataframe
two) From a local drive
To upload from your local drive, kickoff with the post-obit code:
from google.colab import files
uploaded = files.upload() It will prompt yous to select a file. Click on "Choose Files" then select and upload the file. Wait for the file to be 100% uploaded. You should see the proper noun of the file once Colab has uploaded it.
Finally, blazon in the following code to import it into a dataframe (make sure the filename matches the name of the uploaded file).
import io df2 = pd.read_csv(io.BytesIO(uploaded['Filename.csv'])) # Dataset is now stored in a Pandas Dataframe
3) From Google Bulldoze via PyDrive
This is the virtually complicated of the 3 methods. I'll bear witness it for those that have uploaded CSV files into their Google Drive for workflow control. First, type in the post-obit code:
# Lawmaking to read csv file into Colaboratory: !pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials # Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
When prompted, click on the link to get authentication to allow Google to access your Drive. You should encounter a screen with "Google Cloud SDK wants to access your Google Account" at the top. After you let permission, copy the given verification code and paste it in the box in Colab.
Once you have completed verification, become to the CSV file in Google Bulldoze, right-click on it and select "Get shareable link". The link will exist copied into your clipboard. Paste this link into a string variable in Colab.
link = 'https://drive.google.com/open?id=1DPZZQ43w8brRhbEMolgLqOWKbZbE-IQu' # The shareable link What you lot want is the id portion after the equal sign. To get that portion, blazon in the following code:
fluff, id = link.split('=') impress (id) # Verify that you accept everything later on '='
Finally, type in the following code to get this file into a dataframe
downloaded = bulldoze.CreateFile({'id':id})
downloaded.GetContentFile('Filename.csv')
df3 = pd.read_csv('Filename.csv') # Dataset is now stored in a Pandas Dataframe
Final Thoughts
These are three approaches to uploading CSV files into Colab. Each has its benefits depending on the size of the file and how ane wants to organize the workflow. One time the information is in a nicer format like a Pandas Dataframe, you are set to get to piece of work.
Bonus Method — My Drive
Give thanks you and then much for your support. In laurels of this article reaching 50k Views and 25k Reads, I'yard offering a bonus method for getting CSV files into Colab. This one is quite simple and make clean. In your Google Drive ("My Bulldoze"), create a binder called data in the location of your choosing. This is where you will upload your information.
From a Colab notebook, type the following:
from google.colab import bulldoze
drive.mount('/content/bulldoze') Just like with the third method, the commands will bring you to a Google Authentication step. You should see a screen with Google Bulldoze File Stream wants to admission your Google Account. After you allow permission, copy the given verification code and paste it in the box in Colab.
In the notebook, click on the charcoal > on the peak left of the notebook and click on Files. Locate the information folder you created earlier and find your information. Right-click on your data and select Copy Path. Store this copied path into a variable and you are ready to go.
path = "copied path"
df_bonus = pd.read_csv(path) # Dataset is now stored in a Pandas Dataframe
What is great about this method is that you tin can access a dataset from a separate dataset folder you created in your own Google Drive without the actress steps involved in the third method.
Source: https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92#:~:text=Click%20on%20%E2%80%9CChoose%20Files%E2%80%9D%20then,name%20of%20the%20uploaded%20file).
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