How to Use Cursor AI with Jupyter Notebooks (2026)

Cursor AI is one of the best AI-powered code editors available, but its Jupyter Notebook support is not immediately obvious. If you are a data scientist, ML engineer, or analyst who lives in notebooks, this guide shows you how to get the most out of Cursor's AI features with .ipynb files.

We will cover setup, the best workflows for AI-assisted notebook development, and practical tips that make Cursor significantly faster than traditional Jupyter environments.

Does Cursor Support Jupyter Notebooks?

Yes. Cursor is built on VS Code, which has native Jupyter Notebook support. When you open a .ipynb file in Cursor, you get the full notebook experience -- interactive cells, inline output, plots, markdown cells -- plus all of Cursor's AI features on top.

Feature	Cursor + Jupyter	JupyterLab	VS Code + Jupyter
Interactive cells	Yes	Yes	Yes
Inline plots	Yes	Yes	Yes
AI code generation	Yes (Claude/GPT)	No	Limited (Copilot)
AI chat with context	Yes	No	Limited
Agent mode	Yes	No	No
Variable inspector	Yes	Yes	Yes
Kernel management	Yes	Yes	Yes
Extensions	VS Code ecosystem	JupyterLab extensions	VS Code ecosystem

The main advantage of Cursor over JupyterLab is that Cursor's AI understands your entire notebook context -- all cells, outputs, and imported libraries -- when generating code or answering questions.

Step 1: Set Up Cursor for Jupyter

Install Prerequisites

Make sure you have Python and Jupyter installed:

# Install Python (if not already installed)
# macOS
brew install python@3.12

# Install Jupyter and common data science libraries
pip install jupyter ipykernel pandas numpy matplotlib seaborn scikit-learn

Install the Jupyter Extension

Cursor inherits VS Code's extension ecosystem. The Jupyter extension is usually pre-installed, but verify it:

Open Cursor.
Go to Extensions (Cmd+Shift+X / Ctrl+Shift+X).
Search for "Jupyter" and install the official Microsoft Jupyter extension if it is not already installed.
Also install "Python" extension by Microsoft for full language support.

Select a Python Kernel

Open or create a .ipynb file in Cursor.
Click Select Kernel in the top-right corner of the notebook.
Choose Python Environments and select your Python installation or virtual environment.

# Create a dedicated virtual environment for your project
python -m venv .venv
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate   # Windows

# Install your project dependencies
pip install jupyter ipykernel pandas numpy matplotlib

# Register the kernel
python -m ipykernel install --user --name myproject --display-name "My Project"

Step 2: Using Cursor AI Features in Notebooks

Inline Code Generation (Cmd+K / Ctrl+K)

The fastest way to use AI in a notebook cell is Cursor's inline generation. Place your cursor in an empty cell and press Cmd+K (macOS) or Ctrl+K (Windows/Linux):

# Press Cmd+K and type: "load the titanic dataset and show basic statistics"
# Cursor generates:

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')
print(f"Shape: {df.shape}")
print(f"\nColumn types:\n{df.dtypes}")
print(f"\nBasic statistics:\n{df.describe()}")
print(f"\nMissing values:\n{df.isnull().sum()}")

You can also select existing code and press Cmd+K to modify it:

# Select this code and press Cmd+K: "add error handling and make it a reusable function"
# Original:
df = pd.read_csv('data.csv')
df = df.dropna()
df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 50, 100])

# Cursor transforms it to:
def load_and_preprocess(filepath: str, age_bins: list = None) -> pd.DataFrame:
    """Load CSV data, handle missing values, and add age groups."""
    if age_bins is None:
        age_bins = [0, 18, 35, 50, 100]

    try:
        df = pd.read_csv(filepath)
    except FileNotFoundError:
        raise FileNotFoundError(f"Data file not found: {filepath}")

    initial_rows = len(df)
    df = df.dropna()
    dropped = initial_rows - len(df)
    if dropped > 0:
        print(f"Dropped {dropped} rows with missing values ({dropped/initial_rows:.1%})")

    df['age_group'] = pd.cut(df['age'], bins=age_bins)
    return df

Chat Panel (Cmd+L / Ctrl+L)

Open the chat panel with Cmd+L to have a conversation with Cursor about your notebook. The AI can see your entire notebook, including cell outputs:

Example prompts for data science workflows:

"Look at the output of cell 3. Why is the accuracy so low?"
"The plot in cell 7 is hard to read. Improve the visualization."
"Write a function to perform cross-validated grid search for the model in cell 12."
"I'm getting a SettingWithCopyWarning. Fix it."

Agent Mode (Cmd+I / Ctrl+I)

Agent mode is the most powerful feature for notebooks. It can create new cells, edit existing cells, and execute multi-step data science workflows:

Prompt: "Perform a complete EDA on the dataset loaded in cell 1. Create separate cells
for: distribution plots for numeric columns, correlation heatmap, missing value analysis,
and a summary of key findings in markdown."

Agent mode will create 4-5 new cells in your notebook, each with the appropriate code and markdown.

Step 3: Practical Workflows

Workflow 1: Exploratory Data Analysis

Instead of writing EDA code manually, use Cursor to generate it step by step:

# Cell 1: Load data (you write this)
import pandas as pd
df = pd.read_csv('sales_data.csv')
df.head()

Then in the chat panel:

Prompt: "Look at the dataframe output above. Generate a complete EDA in the following cells:
1. Data types and missing values summary
2. Distribution of numeric columns (histograms)
3. Correlation matrix heatmap
4. Top categorical value counts
5. Time series plot if there's a date column"

Cursor generates all five cells with correct code tailored to your specific dataframe columns.

Workflow 2: Debugging Errors

When a cell throws an error, select the error output and ask Cursor to fix it:

# This cell throws: ValueError: could not convert string to float: 'N/A'
from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)  # Error here

Select the error and press Cmd+L:

Prompt: "Fix this error. The training data has string values that need to be handled."

Cursor suggests the fix with full context:

# Cursor's fix:
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np

# Handle non-numeric values
X_train_clean = X_train.copy()
for col in X_train_clean.select_dtypes(include=['object']).columns:
    X_train_clean[col] = X_train_clean[col].replace('N/A', np.nan)
    X_train_clean[col] = pd.to_numeric(X_train_clean[col], errors='coerce')

# Fill remaining NaN values
X_train_clean = X_train_clean.fillna(X_train_clean.median())

model = LinearRegression()
model.fit(X_train_clean, y_train)

Workflow 3: Model Iteration

Use the chat to iterate on model performance:

# Cell output shows: Accuracy: 0.72, F1: 0.68

Prompt: "The model accuracy is 0.72. Suggest and implement 3 different approaches to
improve it. Create a new cell for each approach and compare results in a final summary cell."

Generate a basic plot, then refine it with AI:

# Basic plot
df['sales'].plot()

Prompt: "Make this publication-ready. Add proper labels, title, legend, use a clean
style, add trend line, and annotate the peak value."

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

fig, ax = plt.subplots(figsize=(12, 6))

# Plot data
ax.plot(df.index, df['sales'], color='#2563eb', linewidth=1.5, label='Daily Sales')

# Add trend line
z = np.polyfit(range(len(df)), df['sales'], 1)
p = np.poly1d(z)
ax.plot(df.index, p(range(len(df))), '--', color='#dc2626', linewidth=1, label='Trend')

# Annotate peak
peak_idx = df['sales'].idxmax()
peak_val = df['sales'].max()
ax.annotate(f'Peak: ${peak_val:,.0f}',
            xy=(peak_idx, peak_val),
            xytext=(10, 20), textcoords='offset points',
            fontsize=10, fontweight='bold',
            arrowprops=dict(arrowstyle='->', color='#374151'))

ax.set_title('Daily Sales Performance', fontsize=16, fontweight='bold', pad=15)
ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('Sales ($)', fontsize=12)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

Tips for Better AI Results in Notebooks

1. Keep Cell Outputs Visible

Cursor reads cell outputs to understand your data. Always run cells before asking AI questions so it can see the actual data shapes, column names, and error messages.

2. Use Markdown Cells as Context

Add markdown cells describing what you are trying to do. Cursor uses these as context:

## Objective
Predict customer churn using the telco dataset. Target variable is 'Churn'.
We need at least 85% accuracy for production deployment.

3. Reference Specific Cells

When using chat, reference cells explicitly:

"In cell 5, the merge is producing duplicates. Fix it."
"Use the cleaned dataframe from cell 3 to train a random forest."

4. Use `.py` Files for Complex Logic

For complex utility functions, create separate .py files and import them. Cursor's AI works better with standard Python files for complex code, while notebooks are best for orchestration and visualization.

# utils/preprocessing.py (Cursor AI works great here)
def clean_dataset(df):
    ...

# notebook.ipynb
from utils.preprocessing import clean_dataset
df_clean = clean_dataset(df)

Cursor vs JupyterLab vs Google Colab

Criteria	Cursor + Jupyter	JupyterLab	Google Colab
AI code generation	Excellent	None built-in	Gemini-powered
Offline support	Yes	Yes	No
GPU access	Local only	Local only	Free GPU
Collaboration	Git-based	JupyterHub	Real-time sharing
Extension ecosystem	VS Code (huge)	Jupyter (smaller)	Limited
Performance	Fast (local)	Fast (local)	Variable (cloud)
Cost	$20/mo (Pro) or free tier	Free	Free + paid tiers

Frequently Asked Questions

Can I use Cursor's free tier with Jupyter Notebooks? Yes. The free Hobby plan includes 50 fast premium requests per month, which you can use in notebooks.

Does Cursor support .py percent-format notebooks? Yes. Cursor supports both .ipynb (standard) and .py files with # %% cell markers (percent format). The AI features work with both formats.

Can Cursor read plot outputs? Cursor can see text outputs and dataframe displays. For plots, it can read the code that generated them and suggest improvements, but it does not visually analyze rendered plot images.

What about large datasets? Cursor's AI does not load your data into its context. It reads your code and cell outputs. For large datasets, make sure your display outputs (like df.head(), df.describe()) give the AI enough information to understand your data structure.

Wrapping Up

Cursor turns Jupyter Notebooks from a manual coding environment into an AI-assisted data science workflow. The combination of inline generation, chat with notebook context, and agent mode makes exploratory analysis, model building, and visualization significantly faster.

The key is to keep cell outputs visible, use markdown cells for context, and reference specific cells when chatting with the AI. This gives Cursor the information it needs to generate accurate, context-aware code.

If your data science projects involve AI-generated images or video analysis, try Hypereal AI free -- no credit card required. Its API makes it easy to integrate AI media generation into your Python workflows and notebooks.