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Creating a New Notebook

The New Notebook section allows users to write, execute, and save Python code within an intuitive notebook interface. The platform supports multiple code cells, real-time output, and easy management for machine learning and data analysis tasks.

Steps to Create a New Notebook:​

  1. Start a New Notebook: Navigate to the notebook section and click the New Notebook button to create a fresh notebook.
  2. Provide Notebook Name: Enter a suitable name for the notebook to organize your work.
  3. Add Code Cells: You can add multiple code cells to organize your code execution. Use the up/down arrows to insert more cells.
  4. Write Python Code: Each code cell allows you to write Python code, and the platform supports popular libraries such as numpy, pandas, and matplotlib for data manipulation and visualization.
  5. Execute Code: Once you have written your code, click on Run All Code to execute the code in all cells. The output will be displayed in real-time.
  6. Save Notebook: After reviewing your results, save the notebook for future use and easy retrieval.

Example Code:​

import numpy as np
import matplotlib.pyplot as plt

# Generate random data
ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]

# Create a plot
fig = plt.figure(figsize=(4, 3), facecolor='w')
plt.plot(x, ys, '.')
plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)
plt.title("Sample Visualization", fontsize=10)

Benefits:​

  • Modular Execution: The notebook allows code to be broken down into individual cells, facilitating easy debugging and testing.
  • Real-Time Output: View outputs and results instantly after running code, making iterative development more efficient.
  • Comprehensive Library Support: The platform supports a range of Python libraries, enabling advanced data analysis and machine learning tasks.
  • Easy Code Management: Organize complex workflows across multiple cells and save your work for continuous development.

The New Notebook feature simplifies working with Python code and provides an efficient environment for executing data analysis and machine learning workflows.