Read Csv File From Github in Python
Go Started: three Means to Load CSV files into Colab
Information scientific discipline is cipher without data. Yes, that's obvious. What is non so obvious is the series of steps involved in getting the information into a format which allows you to explore the data. You may be in possession of a dataset in CSV format (short for comma-separated values) but no idea what to practise adjacent. This postal service will help you get started in data science by allowing you to load your CSV file into Colab.
Colab (brusk for Colabor a tory) is a free platform from Google that allows users to code in Python. Colab is essentially 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. Nonetheless, when loading files like CSV files, it requires some extra coding. I volition show you three ways to load a CSV file into Colab and insert it into a Pandas dataframe.
(Note: there are Python packages that acquit common datasets in them. I will not discuss loading those datasets in this article.)
To beginning, log into your Google Account and go to Google Drive. Click on the New push 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 there, import Pandas as shown below (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, and so click on View Raw. Copy the link to the raw dataset and shop information technology as a string variable called url in Colab as shown beneath (a cleaner method but it'southward not necessary). The last pace 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
2) From a local drive
To upload from your local drive, first with the following lawmaking:
from google.colab import files
uploaded = files.upload() It will prompt y'all to select a file. Click on "Cull Files" then select and upload the file. Wait for the file to be 100% uploaded. You should meet the proper name of the file once Colab has uploaded it.
Finally, type in the following lawmaking to import it into a dataframe (make sure the filename matches the proper name of the uploaded file).
import io df2 = pd.read_csv(io.BytesIO(uploaded['Filename.csv'])) # Dataset is at present stored in a Pandas Dataframe
3) From Google Drive via PyDrive
This is the most complicated of the three methods. I'll evidence information technology for those that accept uploaded CSV files into their Google Drive for workflow control. Starting time, type in the following code:
# Code 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.customer 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 let Google to access your Drive. You should run across a screen with "Google Cloud SDK wants to access your Google Account" at the tiptop. Afterwards y'all allow permission, copy the given verification lawmaking and paste it in the box in Colab.
In one case you accept completed verification, get to the CSV file in Google Drive, right-click on it and select "Go shareable link". The link will be copied into your clipboard. Paste this link into a string variable in Colab.
link = 'https://bulldoze.google.com/open?id=1DPZZQ43w8brRhbEMolgLqOWKbZbE-IQu' # The shareable link What yous desire is the id portion later on the equal sign. To get that portion, type in the post-obit code:
fluff, id = link.dissever('=') print (id) # Verify that you have everything afterwards '='
Finally, type in the following code to get this file into a dataframe
downloaded = drive.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 1 wants to organize the workflow. Once the data is in a nicer format like a Pandas Dataframe, you are ready to go to work.
Bonus Method — My Drive
Cheers then much for your back up. In laurels of this article reaching 50k Views and 25k Reads, I'm offering a bonus method for getting CSV files into Colab. This one is quite simple and clean. In your Google Drive ("My Drive"), create a binder chosen information in the location of your choosing. This is where you will upload your data.
From a Colab notebook, blazon the following:
from google.colab import drive
bulldoze.mountain('/content/drive') But like with the 3rd method, the commands volition bring you lot to a Google Authentication step. Y'all should run into a screen with Google Drive File Stream wants to access your Google Account. After you permit permission, re-create the given verification code and paste it in the box in Colab.
In the notebook, click on the charcoal > on the acme left of the notebook and click on Files. Locate the information binder you lot created earlier and find your data. Right-click on your data and select Copy Path. Store this copied path into a variable and you are set to go.
path = "copied path"
df_bonus = pd.read_csv(path) # Dataset is now stored in a Pandas Dataframe
What is great near this method is that you can access a dataset from a separate dataset folder you lot created in your own Google Drive without the extra steps involved in the third method.
Source: https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92
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