In this quick tutorial, we're going to see how to programmatically create a Jupyter Notebook. At the end you will know how to convert markdown, CSV or JSON to Jupyter Notebook.

Generate Notebook with Nbformat

To create a new notebook automatically by nbformat we can use library nbformat. The library can be installed by:

pip install nbformat

To create new notebook we can use the following example:

import nbformat
from nbformat.v4 import new_notebook, new_markdown_cell, new_code_cell

nb = new_notebook()  # create a new empty notebook

# add a markdown cell
cell1 = new_markdown_cell('Markdown cell.')

# add a code cell
cell2 = new_code_cell('print("code cell.")')

# save the notebook to a file
with open('new_notebook.ipynb', 'w') as f:
	nbformat.write(nb, f)


  • new_notebook() - create new notebook
  • new_markdown_cell('Markdown cell.') - add new markdown cell
  • new_code_cell('print("code cell.")') - add code cells

Notebook from markdown, CSV, JSON

We can use pandas to parse data stored as markdown, CSV JSON and create a new jupyter notebook. To do so we can:

  • read the data with Pandas
  • then iterate over the dataframe
  • finally generate the notebook

Example is shown below:

import nbformat
from nbformat.v4 import new_notebook, new_markdown_cell, new_code_cell

nb = new_notebook()
name = '0_julia.ipynb'

for x, cat in enumerate(df.category.unique()):
	cat_sum = cat_sum_img[cat_sum_img['category'] == cat]['category summary'].iloc[0]
	image = cat_sum_img[cat_sum_img['category'] == cat]['image'].iloc[0]
	if '\n' in cat_sum:
    	cat_sum = cat_sum.replace('\n', '\\n').replace('\t', '\\t')
	cell1 = new_markdown_cell(f'## {x}.' + cat_sum)

	for i, row in df[df.category == cat].reset_index().iterrows():
    	description = row.description   
    	code = row.code2   

    	if '\"' in code:
        	# print(code)
        	code = code.replace('\\"', '"')
        	# print(code)
    	msg = '# ' + description + '\n' + code
    	cell2 = new_code_cell(msg)

with open(name, 'w') as f:
	nbformat.write(nb, f)

where data is:

# cat category code code2
0 1.0 Setup import pandas as pd\nimport numpy as np using DataFrames\nusing Statistics\nusing CSV
1 1.0 Setup pip install pandas using Pkg\nPkg.add(\"JSON\")
2 1.0 Setup
3 2.0 Data Structures s = pd.Series(['a', 'b', 'c'], index=[0 , 1, 2]) s = [1, 2, 3]
4 2.0 Data Structures s[0] s[1]