Chapter 18: Pandas Editor
How to use an online Pandas compiler / editor in 2025–2026 — including which platforms are currently the most suitable, how to use them effectively for learning pandas, what you should pay attention to, and realistic examples.
I will explain it as if we are sitting together and I am showing you how to work with pandas right now in the browser — without installing anything on your computer.
Why use an online Pandas editor?
- You don’t need to install Python / Anaconda / VS Code
- You can start in 10 seconds
- You can share your notebook with friends/teachers very easily
- Most platforms have pre-installed pandas, numpy, matplotlib, seaborn
- Excellent for learning, quick experiments, job interview practice, sharing code
The best online platforms for pandas in 2025–2026 (ranked)
| Rank | Platform | Best for | Free tier limits | Pre-installed pandas? | Speed / Stability | Sharing / Export | My recommendation |
|---|---|---|---|---|---|---|---|
| 1 | Google Colab | Serious learning & real projects | Very generous | Yes | ★★★★★ | Very good | ★★★★★ (first choice) |
| 2 | Kaggle Notebooks | Competitions + datasets | Good | Yes | ★★★★☆ | Excellent | ★★★★☆ |
| 3 | Deepnote | Team work, nice UI | Good for individuals | Yes | ★★★★☆ | Very good | ★★★★☆ |
| 4 | JupyterLite (try.jupyter.org) | Very lightweight, no login | Unlimited | Yes | ★★★☆☆ | Limited | ★★★☆☆ |
| 5 | Replit (Python template) | Quick experiments | Free tier has limits | Yes | ★★★☆☆ | Good | ★★★☆☆ |
| 6 | PythonAnywhere | More traditional console + editor | Free tier very limited | Yes | ★★★☆☆ | Limited | ★★☆☆☆ |
My honest recommendation in 2025–2026 → Use Google Colab first → Try Kaggle if you want free datasets + competitions → Use Deepnote if you work with 1–2 friends/teammates
Best choice: Google Colab – step-by-step guide
1. How to open Google Colab
- Go to: https://colab.research.google.com
- Click New Notebook (or File → New notebook)
- You’re ready — no login needed for basic use (but better to login with Google account to save your work)
2. First code you should run in Colab
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# Check Python and pandas version import pandas as pd import numpy as np print("Python version:", sys.version) print("pandas version:", pd.__version__) print("numpy version:", np.__version__) # Very useful: show all columns and more rows pd.set_option('display.max_columns', 100) pd.set_option('display.max_rows', 100) pd.set_option('display.float_format', '{:.2f}'.format) |
3. Realistic beginner example – load, explore, clean, plot
Copy-paste this into a Colab cell and run it:
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import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # 1. Create sample data (like a small CSV) data = { 'name': ['Priya', 'Rahul', 'Ananya', 'Sneha', 'Vikram', 'Meera', 'Arjun', 'Neha'], 'age': [24, 31, 19, 28, 45, 22, 33, 27], 'city': ['Pune', 'Hyderabad', 'Bangalore', 'Mumbai', 'Pune', 'Chennai', 'Mumbai', 'Pune'], 'salary': [72000, 145000, 88000, 112000, 210000, 68000, 95000, 105000], 'department': ['HR', 'IT', 'Sales', 'IT', 'Finance', 'Sales', 'HR', 'Marketing'], 'rating': [4.1, 3.8, 4.6, 4.9, 3.2, 4.0, 4.3, 4.7] } df = pd.DataFrame(data) # 2. Quick exploration print("Shape:", df.shape) print("\nInfo:") df.info() print("\nFirst 5 rows:") display(df.head()) # better than print in Colab # 3. Basic statistics display(df.describe()) # 4. Group by city - average salary city_salary = df.groupby('city')['salary'].agg(['count','mean','max']).round(0) display(city_salary) # 5. Plot: average salary by city plt.figure(figsize=(10,5)) city_salary['mean'].sort_values().plot.barh(color='teal') plt.title('Average Salary by City', fontsize=14) plt.xlabel('Average Salary (₹)') plt.grid(axis='x', alpha=0.3) plt.show() # 6. Scatter: salary vs rating plt.figure(figsize=(9,6)) sns.scatterplot(data=df, x='salary', y='rating', hue='department', size='age', sizes=(40,200)) plt.title('Salary vs Rating by Department') plt.grid(True, alpha=0.3) plt.show() |
4. How to work efficiently in Google Colab
Very useful shortcuts & tips:
- Ctrl + Enter → run current cell
- Shift + Enter → run current cell + move to next
- Alt + Enter → run current cell + create new cell below
- !pip install → install missing library (rarely needed)
- %%time → measure how long cell takes
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%%time # Your code here... df.groupby('city').size() |
Very common first cells people create:
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# Cell 1: imports & settings import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns pd.set_option('display.max_columns', 100) pd.set_option('display.float_format', '{:,.2f}'.format) sns.set_style("whitegrid") print("Ready!") |
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# Cell 2: load or create data # df = pd.read_csv('your_file.csv') # or create sample data like above |
5. Other strong alternatives (if you don’t like Colab)
Kaggle Notebooks → https://www.kaggle.com/code → Huge advantage: many free public datasets → You can use !pip install and GPU/TPU for free (limited hours)
Deepnote → https://deepnote.com → Very nice modern interface → Real-time collaboration is excellent
JupyterLite (no login, very fast start) → https://jupyter.org/try → JupyterLite → Works completely in browser (WebAssembly)
6. Your first mini-project to try right now
Open Colab and try this task:
- Create a DataFrame with at least 10 rows
- Columns: name, age, city, marks_math, marks_science
- Fill with realistic numbers
- Calculate total_marks = math + science
- Create column grade: A (≥90), B (80–89), C (below 80)
- Show average marks per city (bar plot)
- Show scatter plot: math vs science marks, color by grade
Which online platform are you planning to use? (Colab, Kaggle, Deepnote, other…)
If you want, I can give you:
- more complex practice exercises
- a full mini-project with 15–20 steps
- solutions for common errors in online editors
- tips how to share your notebook nicely
Just tell me what you want to do next — I’ll guide you step by step. 😊
