Jupyter-like Features

AI-native Jupyter notebooks, fully managed

How Adeloop notebooks collaborate with AI

Adeloop wraps a familiar Jupyter-style notebook with an AI assistant that sits in-line with your code and results, helping you explore data, summarize insights, and generate visualizations without leaving the notebook.

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Python 3

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Overview

Adeloop provides all the familiar Jupyter-like features you expect, enhanced with cloud capabilities for improved performance and collaboration. Jupyter Features

Jupyter-like features in Adeloop allow you to work with code and data interactively. This includes:

Variable persistence across cells
Sequential execution counting
Rich output display (DataFrames, plots)
Dataset integration
Magic commands and shell access

Core Features

Variable Persistence

Variables defined in one cell execution persist across subsequent executions:

# First cell
x = 10
y = 20

# Second cell - variables x and y are still available
z = x + y
print(z)  # Output: 30

This persistence lets you build analyses incrementally while maintaining your workspace state.

Execution Count Tracking

Each code execution gets a sequential number to track operation order:

# This will be [1]
data = [1, 2, 3]

# This will be [2]
data.append(4)

# This will be [3]
print(len(data))  # Output: 4

Rich Output Display

Adeloop supports interactive visualization of various data types:

import pandas as pd
import matplotlib.pyplot as plt

# DataFrames display as interactive tables
df = pd.DataFrame({
    "A": [1, 2, 3],
    "B": [4, 5, 6]
})
result = df

# Plots render inline
plt.plot(df["A"], df["B"])
plt.title("Sample Plot")
plt.show()

Dataset Access

Selected datasets are automatically available as DataFrames:

# Direct access (recommended)
print(paie.head())
print(paie.shape)

# Also available as df1, df2, etc.
print(df1.head())
print(df2.head())

Best Practices

Variable Management
- Clear unused variables with %reset
- Use descriptive names
- Check memory usage with df.info()
Data Handling
- Preview data before operations
- Use efficient formats (Parquet)
- Handle large datasets in chunks
Code Organization
- Keep cells focused and small
- Add markdown cells for documentation
- Use sequential execution order