Test Jupyter Notebook

November 30, 2025 • 6 min read • Updated: November 30, 2025 • Jupyter Notebook

jupyter python

Notebooks

This is a test notebook to demonstrate Jupyter notebook rendering in Jekyll with the Zer0-Mistakes theme.

Test Jupyter Notebook

This is a test notebook to demonstrate Jupyter notebook rendering in Jekyll with the Zer0-Mistakes theme.

Purpose

This notebook showcases:

Markdown cells with rich formatting
Code cells with Python execution
Mathematical equations using LaTeX
Data visualization with plots
Tables and structured data

%pip install numpy pandas matplotlib

Collecting numpy
  Downloading numpy-2.3.5-cp314-cp314-macosx_14_0_arm64.whl.metadata (62 kB)
  Downloading numpy-2.3.5-cp314-cp314-macosx_14_0_arm64.whl.metadata (62 kB)
Collecting pandas
Collecting pandas
  Downloading pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (91 kB)
  Downloading pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (91 kB)
Collecting matplotlib
Collecting matplotlib
  Downloading matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl.metadata (11 kB)
Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from pandas) (2.9.0.post0)
  Downloading matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl.metadata (11 kB)
Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from pandas) (2.9.0.post0)
Collecting pytz>=2020.1 (from pandas)
  Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)
Collecting pytz>=2020.1 (from pandas)
  Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)
Collecting tzdata>=2022.7 (from pandas)
  Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)
Collecting tzdata>=2022.7 (from pandas)
  Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)
Collecting contourpy>=1.0.1 (from matplotlib)
  Downloading contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (5.5 kB)
Collecting cycler>=0.10 (from matplotlib)
Collecting contourpy>=1.0.1 (from matplotlib)
  Downloading contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (5.5 kB)
Collecting cycler>=0.10 (from matplotlib)
  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)
  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)
Collecting fonttools>=4.22.0 (from matplotlib)
  Downloading fonttools-4.61.0-cp314-cp314-macosx_10_15_universal2.whl.metadata (113 kB)
Collecting fonttools>=4.22.0 (from matplotlib)
  Downloading fonttools-4.61.0-cp314-cp314-macosx_10_15_universal2.whl.metadata (113 kB)
Collecting kiwisolver>=1.3.1 (from matplotlib)
  Downloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl.metadata (6.3 kB)
Collecting kiwisolver>=1.3.1 (from matplotlib)
  Downloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl.metadata (6.3 kB)
Requirement already satisfied: packaging>=20.0 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from matplotlib) (25.0)
Requirement already satisfied: packaging>=20.0 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from matplotlib) (25.0)
Collecting pillow>=8 (from matplotlib)
  Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl.metadata (8.8 kB)
Collecting pillow>=8 (from matplotlib)
  Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl.metadata (8.8 kB)
Collecting pyparsing>=3 (from matplotlib)
  Downloading pyparsing-3.2.5-py3-none-any.whl.metadata (5.0 kB)
Collecting pyparsing>=3 (from matplotlib)
  Downloading pyparsing-3.2.5-py3-none-any.whl.metadata (5.0 kB)
Requirement already satisfied: six>=1.5 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
Downloading numpy-2.3.5-cp314-cp314-macosx_14_0_arm64.whl (5.1 MB)
[?25l   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m0.0/5.1 MB[0m [31m?[0m eta [36m-:--:--[0mRequirement already satisfied: six>=1.5 in /Users/bamr87/github/zer0-mistakes/.venv/lib/python3.14/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
Downloading numpy-2.3.5-cp314-cp314-macosx_14_0_arm64.whl (5.1 MB)
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m5.1/5.1 MB[0m [31m6.6 MB/s[0m  [33m0:00:00[0m eta [36m0:00:01[0mm
[?25hDownloading pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl (10.8 MB)
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m5.1/5.1 MB[0m [31m6.6 MB/s[0m  [33m0:00:00[0m
[?25hDownloading pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl (10.8 MB)
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m10.8/10.8 MB[0m [31m3.9 MB/s[0m  [33m0:00:02[0mm0:00:01[0m0:01[0mm
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m10.8/10.8 MB[0m [31m3.9 MB/s[0m  [33m0:00:02[0mm0:00:01[0m
[?25hDownloading matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl (8.1 MB)
[?25l   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m0.0/8.1 MB[0m [31m?[0m eta [36m-:--:--[0mDownloading matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl (8.1 MB)
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m8.1/8.1 MB[0m [31m7.0 MB/s[0m  [33m0:00:01[0m eta [36m0:00:01[0m
[?25hDownloading contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl (273 kB)
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m8.1/8.1 MB[0m [31m7.0 MB/s[0m  [33m0:00:01[0m
[?25hDownloading contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl (273 kB)
Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)
Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)
Downloading fonttools-4.61.0-cp314-cp314-macosx_10_15_universal2.whl (2.8 MB)
[?25l   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m0.0/2.8 MB[0m [31m?[0m eta [36m-:--:--[0mDownloading fonttools-4.61.0-cp314-cp314-macosx_10_15_universal2.whl (2.8 MB)
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m2.8/2.8 MB[0m [31m4.3 MB/s[0m  [33m0:00:00[0m eta [36m0:00:01[0m
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[?25hDownloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl (64 kB)
Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl (4.7 MB)
Downloading pyparsing-3.2.5-py3-none-any.whl (113 kB)
Downloading kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl (64 kB)
Using cached pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl (4.7 MB)
Downloading pyparsing-3.2.5-py3-none-any.whl (113 kB)
Using cached pytz-2025.2-py2.py3-none-any.whl (509 kB)
Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)
Using cached pytz-2025.2-py2.py3-none-any.whl (509 kB)
Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)
Installing collected packages: pytz, tzdata, pyparsing, pillow, numpy, kiwisolver, fonttools, cycler, pandas, contourpy, matplotlib
Installing collected packages: pytz, tzdata, pyparsing, pillow, numpy, kiwisolver, fonttools, cycler, pandas, contourpy, matplotlib
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m11/11[0m [matplotlib]1[0m [matplotlib]
[1A[2KSuccessfully installed contourpy-1.3.3 cycler-0.12.1 fonttools-4.61.0 kiwisolver-1.4.9 matplotlib-3.10.7 numpy-2.3.5 pandas-2.3.3 pillow-12.0.0 pyparsing-3.2.5 pytz-2025.2 tzdata-2025.2
[2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m11/11[0m [matplotlib]1[0m [matplotlib]
[1A[2KSuccessfully installed contourpy-1.3.3 cycler-0.12.1 fonttools-4.61.0 kiwisolver-1.4.9 matplotlib-3.10.7 numpy-2.3.5 pandas-2.3.3 pillow-12.0.0 pyparsing-3.2.5 pytz-2025.2 tzdata-2025.2
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

# Import required libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

print("Libraries imported successfully!")
print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")

Libraries imported successfully!
NumPy version: 2.3.5
Pandas version: 2.3.3

Mathematical Equations

Jupyter notebooks support LaTeX equations via MathJax:

Inline equation: $E = mc^2$

Display equation:

\[\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi}\]

More complex equation:

\[f(x) = \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^2}\]

# Generate sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create a simple plot
plt.figure(figsize=(10, 6))
plt.plot(x, y1, label='sin(x)', linewidth=2)
plt.plot(x, y2, label='cos(x)', linewidth=2)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Trigonometric Functions')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

print("Plot generated successfully!")

png

Plot generated successfully!

Data Tables

Pandas DataFrames render as nice HTML tables:

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [25, 30, 35, 28, 32],
    'City': ['New York', 'San Francisco', 'Chicago', 'Boston', 'Seattle'],
    'Score': [95, 87, 92, 88, 91]
}

df = pd.DataFrame(data)
print(f"DataFrame shape: {df.shape}")
df

DataFrame shape: (5, 4)

	Name	Age	City	Score
0	Alice	25	New York	95
1	Bob	30	San Francisco	87
2	Charlie	35	Chicago	92
3	David	28	Boston	88
4	Eve	32	Seattle	91

Code Formatting

Jupyter notebooks display code with proper syntax highlighting:

Lists and Loops

# Fibonacci sequence generator
def fibonacci(n):
    """Generate Fibonacci sequence up to n terms."""
    fib = [0, 1]
    while len(fib) < n:
        fib.append(fib[-1] + fib[-2])
    return fib

# Generate and display first 10 Fibonacci numbers
fib_sequence = fibonacci(10)
print("First 10 Fibonacci numbers:")
for i, num in enumerate(fib_sequence, 1):
    print(f"F({i}) = {num}")

First 10 Fibonacci numbers:
F(1) = 0
F(2) = 1
F(3) = 1
F(4) = 2
F(5) = 3
F(6) = 5
F(7) = 8
F(8) = 13
F(9) = 21
F(10) = 34

Conclusion

This test notebook demonstrates the key features of Jupyter notebook rendering in Jekyll:

✅ Markdown formatting with headers, lists, and emphasis
✅ LaTeX equations for mathematical notation
✅ Code cells with syntax highlighting
✅ Data visualization with matplotlib plots
✅ Data tables with pandas DataFrames
✅ Rich output from code execution

The notebook conversion system:

Converts .ipynb files to Jekyll-compatible Markdown
Extracts images to assets/images/notebooks/
Adds proper front matter with metadata
Maintains code cell formatting and outputs
Preserves mathematical equations for MathJax rendering

Next Steps:

Add more complex visualizations
Include interactive widgets (note: will be static in Jekyll)
Test with larger datasets
Verify GitHub Pages compatibility

Layout	`notebook`
Collection	`notebooks`
Path	`_notebooks/test-notebook.md`
URL	`/notebooks/test-notebook/`
Date	`2025-11-30`

Settings

Language

Color Mode

Theme Skin

Background

Environment

Theme & Build

Admin

Page Location

Page Info

Source Code

Test Jupyter Notebook

Table of Contents

Test Jupyter Notebook

Test Jupyter Notebook

Purpose

Mathematical Equations

Data Tables

Code Formatting

Lists and Loops

Conclusion

Comments

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