LabEx VM Jupyter Interface

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Jupyter Interface Preview

The Jupyter environment is particularly useful for:

1. Data analysis and visualization projects using Python libraries (Pandas, Matplotlib, etc.)
2. Machine learning and deep learning experiments
3. Interactive code execution with immediate feedback
4. Creating documented workflows combining code and markdown
5. Educational content with explanations and executable examples

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Access the Interface

1. After starting a Jupyter-enabled lab, click the “Notebook” tab at the top of the page
2. Wait for the Jupyter interface to fully load
3. You’ll see the familiar Jupyter Notebook interface in your browser

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Interface Layout

The Jupyter interface consists of several key components:

1. Main Work Area:
   • Notebook cells (code and markdown)
   • Output display
   • Toolbar with common actions
2. Top Menu Bar:
   • File operations
   • Cell manipulation
   • Kernel controls

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Working with Notebooks

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Cell Types

1. Code Cells:

   • Write and execute Python code
   • View output directly below the cell
   • Use Shift+Enter to execute

2. Markdown Cells:

   • Document your work
   • Add explanations and notes
   • Support mathematical equations using LaTeX

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Common Operations

1. Create new cells:

   • Click the + button in the toolbar
   • Use keyboard shortcut B (below) or A (above)

2. Run cells:

   • Click the play button
   • Use Shift+Enter
   • Use Cell menu options

3. Change cell type:

   • Use the dropdown in the toolbar
   • Keyboard shortcuts: Y (code), M (markdown)

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Usage Scenarios

import pandas as pd
import matplotlib.pyplot as plt

# Read data
df = pd.read_csv('data.csv')

# Create visualization
plt.figure(figsize=(10, 6))
df['column'].plot(kind='bar')
plt.title('Data Visualization')
plt.show()

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate
score = model.score(X_test, y_test)
print(f"Model accuracy: {score:.2f}")

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Frequently Asked Questions

!pip install package-name