Colab : Free Cloud Platfrom from Google

Advantage of using Colab the colud platform for Python 🔝

1. Resource Availability:
   • Computational Power: Colab provides access to powerful GPUs and TPUs.
   • Memory: Colab allows for more memory, useful for handling large datasets and computations.
2. Free Access to GPU and TPU:
   • Colab provides free access to GPU and TPU resources, beneficial for machine learning tasks.
3. Collaboration and Sharing:
   • Colab facilitates real-time collaboration and easy sharing of notebooks.
   • Multiple users can work on the same notebook simultaneously.
4. Integration with Google Services:
   • Colab integrates seamlessly with Google Drive and other Google services.
5. Pre-installed Libraries and Packages:
   • Colab comes with pre-installed popular Python libraries and packages.
6. No Installation Required:
   • Colab is accessed through a web browser, eliminating the need for local installations.
7. Automatic Saving and Version History:
   • Colab automatically saves work to Google Drive and provides version history.
8. Easier Access to Data:
   • Colab allows seamless access to data stored in cloud services.
9. Learning and Exploration:
   • Colab is a great platform for learning and exploration. It is consistently accessible and instantly responsive the moment you initiate or embark on your journey.
   • Beginners can start coding without worrying about environment setup.

Adding Colab to your Google drive 🔝

New > More > Google Colaboratory 

New > More > Connect More Apps

Downloading files by using wget 🔝

# download my_db.db SQLite Database from plus2net.com
!wget https://www.plus2net.com/python/download/my_db.db
# !wget https://www.plus2net.com/python/download/student.xlsx # Excel file download
# !wget https://www.plus2net.com/python/download/student.csv # CSV file download 

Version of Python in Colab 🔝

!python --version

Upload and download file from local system to colab 🔝

from google.colab import files
uploaded = files.upload()

files.download('fish-market.jpg')

fob=open('data1.txt','w')
fob.write("this is a new text just written")
fob.close()
files.download('data1.txt')

Adding CSV or Excel files to your code. 🔝

1. Upload your CSV or Excel file to any folder in google drive. We created one folder inside our Colab Notbooks folder and named it as downloads. You can give your own name to the folders.
2. Now you have to mount the drive to your colab file. Run this code.

from google.colab import drive
drive.mount('/content/drive')

You will be shown one link and below it there will be an text box asking for authorization code. Visit the link and collect ( copy ) your authorization code. You must be already logged in to your gmail account to get the code.
Paste the code inside textbox and then press enter.
Now your drive is mounted.
3. 
   
   In your left panel you can see drive icon. Expand the directory structure to reach and select your file you want to use. Right click and use copy path.
   Now use this path to create a variable to use in our read_excel() or read_csv() functions.

   import pandas as pd
   path='/content/drive/My Drive/Colab Notebooks/downloads/my_file.xlsx'
   my_data = pd.read_excel(path)

!wget https://raw.githubusercontent.com/adeshpande3/Pandas-Tutorial/master/RegularSeasonCompactResults.csv

Checking & Installing libraries in Colab 🔝

%pip freeze

import imp
try:
    imp.find_module('reportlab') # check reportlab library for PDF creation
    found = True
except ImportError:
    found = False
print(found)

!pip install pytrends

🔧 AI Features in Google Colab for Code Assistance 🔝

1. Code Completion (AI-Powered)
   Google Colab now includes AI-based autocompletion (similar to Copilot), suggesting code as you type. It understands context, variable names, and patterns from the notebook to suggest relevant lines of code.
2. Code Generation from prompt
   You can write a natural language prompt like create a bar chart from the given data in sqlite database and the AI will generate the corresponding Python code.
3. Context-Aware Code Suggestions
   Based on the code written above, Colab AI suggests functions, parameters, and data manipulation snippets tailored to your current context.
4. Debugging Assistant
   When an error occurs, the Colab AI agent can explain the traceback in simple language, suggest possible fixes, and even generate corrected code snippets.
5. Ask AI (Data Science Agent)
   Located in the sidebar, this lets you type natural language questions like:
   • “Plot the distribution of column X”
   • “Find outliers in my dataset”
   • “What is the correlation between A and B?”
   The AI responds with code you can run directly.
6. Explain Code Blocks
   Right-click on any code cell and choose "Explain Code" to get a human-readable summary of what the code does. Useful for beginners or reviewing shared notebooks.
7. Bug Fix Suggestions
   If your code fails, Colab AI might show a suggestion like: "Did you mean df['column_name'] instead of df.column_name?"
8. Natural Language to Chart/Plot
   You can say “create a line chart of monthly revenue” and Colab will write matplotlib/seaborn code based on your data.
9. Code Snippet Injection
   With context-aware insertions, Colab suggests full blocks of reusable code (e.g., data cleaning functions, visualizations, model evaluation).
10. Auto Imports & Fixes
    Colab often detects missing imports and suggests the correct ones, like “Import pandas as pd” if you’re using a DataFrame but forgot the import.

Using SQLite in Colab 🔝

Creating PDF files at Colab 🔝

Using Secrets in Google Colab 🔝

Google Colab provides a Secrets feature to securely store sensitive data like API keys or credentials. These values are encrypted and not visible when sharing notebooks.

To add a secret, use the key icon on the left sidebar, name your secret (e.g., OPENAI_API_KEY), and enter its value. Toggle access for each notebook as needed.

You can retrieve a secret in your notebook using:

from google.colab import userdata
api_key = userdata.get('OPENAI_API_KEY')

To use the secret as an environment variable:

import os
os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')

1. You must explicitly toggle access for each notebook in the Secrets panel.
2. This ensures that even though the secret exists globally in your account, only the notebooks you authorize can use it.

This keeps your keys hidden and secure while enabling seamless integration with APIs.

Subhendu Mohapatra

Author

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