Free Tool
Data Science Python Code Snippets
Ready-to-use Python code for common data science tasks. Copy any snippet, or let runcell generate custom code tailored to your dataset.
Load & Inspect a CSV Dataset
Read a CSV file with pandas and display shape, data types, and first rows.
import pandas as pd
df = pd.read_csv("your_data.csv")
print(f"Shape: {df.shape}")
print(f"\nData types:\n{df.dtypes}")
print(f"\nFirst 5 rows:")
df.head()Summary Statistics
Generate descriptive statistics for all numerical and categorical columns.
# Descriptive statistics for numerical columns
df.describe()
# Include non-numeric columns
df.describe(include='all')Check Missing Values
Count and visualize missing values across all columns in a DataFrame.
import matplotlib.pyplot as plt
# Count missing values per column
missing = df.isnull().sum()
missing = missing[missing > 0].sort_values(ascending=False)
print(missing)
# Visualize
missing.plot(kind='bar', figsize=(10, 4),
title='Missing Values by Column')
plt.ylabel('Count')
plt.tight_layout()
plt.show()Correlation Matrix Heatmap
Compute and display pairwise correlation between numerical features as a heatmap.
import seaborn as sns
import matplotlib.pyplot as plt
corr = df.select_dtypes(include='number').corr()
plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, cmap='coolwarm',
center=0, fmt='.2f')
plt.title('Feature Correlation Matrix')
plt.tight_layout()
plt.show()Need code customized for your dataset? Try runcell free — describe your task and the AI agent writes, runs, and iterates on the code for you.
Features & Use Cases
See What the AI Agent Can Do
From learning new algorithms to automating entire workflows — runcell handles the heavy lifting while you focus on the science.
Interactive Learning Mode
AI teacher that explains concepts with live examples - perfect for learning differences between algorithms like K-means vs DBSCAN.
Autonomous Agent Mode
AI takes full control of your notebook, executing cells and completing complex workflows automatically.
Bioinformatics visualization
Help with coding for domain experts, you focus on idea, agent handles coding.
AI-Enhanced Jupyter
Ask questions about your code, get AI-generated solutions, and receive smart recommendations for next steps.
Get Started
Up and Running in Three Steps
Install the Extension
One pip command adds runcell to any JupyterLab environment. No migration, no config files.
Describe Your Task
Tell the agent what you need in plain English — “clean this column”, “plot a heatmap”, “train an XGBoost model.”
Review & Iterate
The agent writes code, executes cells, and shows results. Tweak, ask follow-ups, or let it keep going autonomously.
Why runcell
Put Data Science AI to Work in Your Notebook
Modern research teams need tools that keep pace with the scale of their ideas. runcell introduces a data science AI companion that lives directly in JupyterLab, watching every cell and waiting to assist. When you describe a transformation in plain language, the agent proposes code that matches your style and references the variables already in memory. This tight feedback loop helps scientists stay in the flow and keeps experiments moving instead of stalling on boilerplate.
The data science AI inside runcell constantly scans the surrounding notebook context. It reads markdown notes, interprets charts, and follows the evolution of your datasets. Rather than offering generic snippets, the agent tailors suggestions to the structure of your project and the libraries you rely on most. You can ask it to plot a distribution, clean a column, or engineer features for a machine learning experiment, and it will write the code and run the cells so you can immediately inspect the results.
Building a rich analysis often requires combining many small tasks, and the runcell agent excels at stringing those tasks together. It can refactor earlier cells, add missing imports, and suggest reusable functions that make notebooks cleaner. Beginners get interactive guidance, while veterans enjoy faster execution for routine chores.
Security and privacy are baked into the design. The core data science AI runs in the browser, and heavy computations are delegated to secure APIs that never retain your data. This lets enterprises benefit from cutting-edge models without compromising control. Whether you are exploring sensitive health records or proprietary financial metrics, runcell keeps the workflow compliant and confined to your environment.
Collaboration becomes easier when everyone can see how the analysis unfolded. runcell annotates key cells, summarizes outputs, and encourages consistent documentation. Share a notebook with colleagues and they will immediately understand the steps the agent took, making peer review and onboarding smoother.
FAQ
Frequently Asked Questions About Data Science AI
What is a data science AI agent?
A data science AI agent is an intelligent assistant that automates common data science tasks — writing Python code, running cells, cleaning datasets, building visualizations, and training ML models. runcell is a data science AI agent that works directly inside JupyterLab, understanding your notebook context and generating production-ready code from natural-language prompts.
How does AI change data science workflows?
AI eliminates the repetitive parts of data science — writing boilerplate code, formatting DataFrames, and creating standard charts. With a data science AI agent like runcell, you describe what you want in plain English and the agent generates, executes, and iterates on the code. This lets data scientists focus on interpreting results and making decisions rather than debugging syntax.
Can I use it with my existing JupyterLab setup?
Yes. runcell installs as a JupyterLab extension and works alongside your existing notebooks, kernels, and libraries. There is nothing to migrate — install the extension, sign in, and the AI agent is ready to assist in any open notebook.
What data science tasks can the AI automate?
runcell automates exploratory data analysis, data cleaning and wrangling, feature engineering, statistical testing, visualization, model training and evaluation, and notebook documentation. You can also ask the agent to refactor code, add missing imports, or explain complex outputs.
Is a data science AI agent suitable for beginners?
Absolutely. The AI agent explains each code snippet it generates, so beginners learn best practices while getting work done. More experienced data scientists benefit from faster execution of routine tasks and less time spent on boilerplate code.
How is my data protected when using the AI?
runcell executes code locally in your Jupyter environment. Your datasets remain on your machine and are never uploaded to external servers without explicit permission. The architecture is designed for enterprise use, including work with sensitive or proprietary data.
What Python libraries does runcell support?
runcell works with any Python library installed in your Jupyter kernel. It is particularly effective with pandas, NumPy, scikit-learn, matplotlib, seaborn, XGBoost, TensorFlow, PyTorch, and other popular data science packages. The AI agent reads your imports and adapts its suggestions accordingly.
How does runcell compare to ChatGPT for data science?
Unlike ChatGPT, runcell runs inside JupyterLab and has direct access to your notebook context — variables, DataFrames, and execution state. It writes code that references your actual data and executes it in place, so there is no copy-pasting or context-switching. This makes it faster and more accurate for real data science work.