How to Use Pandas Online: No Installation Required

Q: What is create and explore a dataframe in Python?

A Pandas DataFrame is a table with named columns and a row index. You can build one from a dictionary, a list of dicts, a CSV, or many other sources.

Q: What is load data from a csv in Python?

In a real project you'd use pd.read_csv("file.csv"). In the browser, use io.StringIO to wrap an inline CSV string - the same API, perfect for testing and learning.

Q: What is groupby and aggregate in Python?

groupby splits your DataFrame into groups, lets you apply a function to each, and combines the results. This is the Pandas equivalent of SQL's GROUP BY - and it's one of the most powerful tools in data analysis.

Q: What is merge two dataframes in Python?

Pandas merge works like a SQL JOIN - combine two DataFrames on a shared key. Supports inner, left, right, and outer joins.

Beginner15 min0 exercises0 XP

You want to load a CSV, filter some rows, group by category, and plot the result. The standard path: install Python, create a virtual environment, run pip install pandas matplotlib, open Jupyter. Twenty minutes later you're still fixing a conda conflict.

The shortcut: open pythoncompiler.io in a browser tab. Pandas, NumPy, and Matplotlib are pre-loaded. No installation, no account, no waiting. Your first analysis can be running in under 60 seconds.

This guide walks through the most useful Pandas operations - all with runnable examples you can edit and execute directly on this page.

Create and Explore a DataFrame

A Pandas DataFrame is a table with named columns and a row index. You can build one from a dictionary, a list of dicts, a CSV, or many other sources.

Create and inspect a DataFrame

import pandas as pd

# Build a DataFrame from a dictionary

sales = pd.DataFrame({

'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],

'region': ['North', 'South', 'North', 'South', 'North', 'South'],

'revenue': [12400, 9800, 15600, 11200, 18900, 14300],

'units': [124, 98, 156, 112, 189, 143],

})

print(sales)

print()

print("Shape:", sales.shape)

print("Columns:", list(sales.columns))

print()

print(sales.describe())

Output

  month region  revenue  units
0   Jan  North    12400    124
1   Feb  South     9800     98
2   Mar  North    15600    156
3   Apr  South    11200    112
4   May  North    18900    189
5   Jun  South    14300    143

Shape: (6, 4)
Columns: ['month', 'region', 'revenue', 'units']

            revenue       units
count      6.000000    6.000000
mean   13700.000000  137.000000
std     3291.200389   32.912004
min     9800.000000   98.000000
25%    11500.000000  115.000000
50%    13350.000000  133.500000
75%    15275.000000  152.750000
max    18900.000000  189.000000

Load Data from a CSV

In a real project you'd use pd.read_csv("file.csv"). In the browser, use io.StringIO to wrap an inline CSV string - the same API, perfect for testing and learning.

Load CSV data inline

import pandas as pd

import io

csv_data = """product,category,price,stock

Laptop,Electronics,999,45

Headphones,Electronics,79,312

Desk,Furniture,349,28

Chair,Furniture,189,74

Notebook,Stationery,5,890

Pen,Stationery,2,2100"""

df = pd.read_csv(io.StringIO(csv_data))

print(df)

print()

print("Data types:")

print(df.dtypes)

Output

      product     category  price  stock
0      Laptop  Electronics    999     45
1  Headphones  Electronics     79    312
2        Desk    Furniture    349     28
3       Chair    Furniture    189     74
4    Notebook   Stationery      5    890
5         Pen   Stationery      2   2100

Data types:
product     object
category    object
price        int64
stock        int64
dtype: object

Filter, Sort, and Select

Boolean indexing is the core of Pandas filtering. Pass any True/False condition inside df[...] to select matching rows.

Filter and sort data

import pandas as pd

import io

csv_data = """product,category,price,stock

Laptop,Electronics,999,45

Headphones,Electronics,79,312

Desk,Furniture,349,28

Chair,Furniture,189,74

Notebook,Stationery,5,890

Pen,Stationery,2,2100"""

df = pd.read_csv(io.StringIO(csv_data))

# Filter: price > 100

expensive = df[df['price'] > 100]

print("Items over $100:")

print(expensive[['product', 'price']])

print()

# Multiple conditions

electronics_cheap = df[(df['category'] == 'Electronics') & (df['price'] < 200)]

print("Affordable electronics:")

print(electronics_cheap)

print()

# Sort by price descending

print("By price (high to low):")

print(df.sort_values('price', ascending=False)[['product', 'price']].head(3))

Output

Items over $100:
  product  price
0  Laptop    999
2    Desk    349
3   Chair    189

Affordable electronics:
      product     category  price  stock
1  Headphones  Electronics     79    312

By price (high to low):
  product  price
0  Laptop    999
2    Desk    349
3   Chair    189

GroupBy and Aggregate

groupby splits your DataFrame into groups, lets you apply a function to each, and combines the results. This is the Pandas equivalent of SQL's GROUP BY - and it's one of the most powerful tools in data analysis.

GroupBy with multiple aggregations

import pandas as pd

sales = pd.DataFrame({

'month': ['Jan','Feb','Mar','Apr','May','Jun','Jan','Feb','Mar'],

'region': ['North','North','North','South','South','South','West','West','West'],

'revenue': [12400, 9800, 15600, 11200, 18900, 14300, 8700, 11400, 9200],

'units': [124, 98, 156, 112, 189, 143, 87, 114, 92],

})

# Total revenue per region

by_region = sales.groupby('region')['revenue'].sum().sort_values(ascending=False)

print("Revenue by region:")

print(by_region)

print()

# Multiple aggregations

summary = sales.groupby('region').agg(

total_revenue=('revenue', 'sum'),

avg_revenue=('revenue', 'mean'),

total_units=('units', 'sum'),

months=('month', 'count'),

).round(0)

print("Full summary:")

print(summary)

Output

Revenue by region:
region
South    44400
North    37800
West     29300
Name: revenue, dtype: int64

Full summary:
        total_revenue  avg_revenue  total_units  months
region                                                 
North           37800      12600.0          378       3
South           44400      14800.0          444       3
West            29300       9767.0          293       3

Merge Two DataFrames

Pandas merge works like a SQL JOIN - combine two DataFrames on a shared key. Supports inner, left, right, and outer joins.

Merge orders with customer data

import pandas as pd

orders = pd.DataFrame({

'order_id': [1, 2, 3, 4, 5],

'customer_id': [101, 102, 101, 103, 102],

'amount': [250, 80, 175, 320, 95],

})

customers = pd.DataFrame({

'customer_id': [101, 102, 103],

'name': ['Alice', 'Bob', 'Charlie'],

'tier': ['Gold', 'Silver', 'Gold'],

})

# Inner join on customer_id

merged = pd.merge(orders, customers, on='customer_id')

print(merged)

print()

# Total spend per customer

total = merged.groupby('name')['amount'].sum().sort_values(ascending=False)

print("Total spend per customer:")

print(total)

Output

   order_id  customer_id  amount     name    tier
0         1          101     250    Alice    Gold
1         2          102      80      Bob  Silver
2         3          101     175    Alice    Gold
3         4          103     320  Charlie    Gold
4         5          102      95      Bob  Silver

Total spend per customer:
name
Alice      425
Charlie    320
Bob        175
Name: amount, dtype: int64

Visualize with Matplotlib

Pandas integrates directly with Matplotlib. Call .plot() on any Series or DataFrame column to create a chart. The plot renders in the output panel below your code.

Multi-chart Pandas visualization

import pandas as pd

import matplotlib.pyplot as plt

sales = pd.DataFrame({

'month': ['Jan','Feb','Mar','Apr','May','Jun'],

'North': [12400, 9800, 15600, 17200, 18900, 21400],

'South': [9800, 11200, 10400, 12800, 14300, 16100],

'West': [8700, 11400, 9200, 13500, 12800, 15200],

}).set_index('month')

fig, axes = plt.subplots(1, 2, figsize=(12, 4))

# Line chart: revenue over time by region

sales.plot(ax=axes[0], marker='o', title='Monthly Revenue by Region')

axes[0].set_ylabel('Revenue ($)')

axes[0].grid(True, alpha=0.3)

# Bar chart: total by region

sales.sum().plot(kind='bar', ax=axes[1], color=['steelblue','coral','seagreen'],

title='Total Revenue by Region', rot=0)

axes[1].set_ylabel('Total Revenue ($)')

plt.tight_layout()

plt.show()

Output

Chart drawn by: Multi-chart Pandas visualization

Install Additional Packages

Pandas, NumPy, and Matplotlib load automatically. For more libraries - scikit-learn, scipy, statsmodels, seaborn - open the Packages panel on the right side of the editor and install them on demand. Everything runs locally in your browser - no server, no rate limits.

Linear regression with scikit-learn (install from Packages panel)

# After installing scikit-learn from the Packages panel:

import pandas as pd

import numpy as np

from sklearn.linear_model import LinearRegression

# Simulate housing data

np.random.seed(42)

n = 100

df = pd.DataFrame({

'sqft': np.random.randint(500, 3000, n),

'beds': np.random.randint(1, 6, n),

'price': np.random.randint(100000, 800000, n),

})

df['price'] = df['sqft'] * 200 + df['beds'] * 15000 + np.random.randn(n) * 20000

X = df[['sqft', 'beds']]

y = df['price']

model = LinearRegression().fit(X, y)

print(f"R² score: {model.score(X, y):.3f}")

print(f"Price per sqft: ${model.coef_[0]:,.0f}")

print(f"Price per bedroom: ${model.coef_[1]:,.0f}")

print(f"Predicted price (1200sqft, 3 beds): ${model.predict([[1200, 3]])[0]:,.0f}")

Output

R² score: 0.979
Price per sqft: $201
Price per bedroom: $15,751
Predicted price (1200sqft, 3 beds): $283,690
/lib/python3.13/site-packages/sklearn/utils/validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(

Create and Explore a DataFrame

Load Data from a CSV

Filter, Sort, and Select

GroupBy and Aggregate

Merge Two DataFrames

Visualize with Matplotlib

Install Additional Packages

Related Tutorials