Convert IPYNB to MEDIAWIKI

Practical Examples

Example 1: Wiki Article from Data Analysis

Input IPYNB file (notebook.ipynb):

# Markdown Cell:
# Population Growth Analysis
## Methodology
We analyzed census data from **50 countries** over *three decades*.

# Code Cell:
import pandas as pd
df = pd.read_csv('census.csv')
growth = df.groupby('continent')['growth_rate'].mean()
print(growth.to_string())

# Output:
continent
Africa      2.54
Asia        1.12
Europe      0.23
Americas    0.89

Output MEDIAWIKI file (notebook.mediawiki):

== Population Growth Analysis ==

=== Methodology ===

We analyzed census data from '''50 countries''' over ''three decades''.


import pandas as pd
df = pd.read_csv('census.csv')
growth = df.groupby('continent')['growth_rate'].mean()
print(growth.to_string())


continent
Africa      2.54
Asia        1.12
Europe      0.23
Americas    0.89


[[Category:Data Analysis]]

Example 2: Knowledge Base Entry from ML Research

Input IPYNB file (analysis.ipynb):

# Markdown Cell:
# Decision Tree Classification
Decision trees are a supervised learning method used for classification.

## Algorithm Parameters
- **max_depth**: Maximum tree depth
- **min_samples_split**: Minimum samples to split a node

# Code Cell:
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(max_depth=5)
model.fit(X_train, y_train)
print(f"Feature importances: {model.feature_importances_[:3]}")
print(f"Tree depth: {model.get_depth()}")

# Output:
Feature importances: [0.42 0.31 0.18]
Tree depth: 5

Output MEDIAWIKI file (analysis.mediawiki):

== Decision Tree Classification ==

Decision trees are a supervised learning method used for classification.

=== Algorithm Parameters ===

* '''max_depth''': Maximum tree depth
* '''min_samples_split''': Minimum samples to split a node


from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(max_depth=5)
model.fit(X_train, y_train)
print(f"Feature importances: {model.feature_importances_[:3]}")
print(f"Tree depth: {model.get_depth()}")


Feature importances: [0.42 0.31 0.18]
Tree depth: 5


{{Infobox algorithm
| name = Decision Tree
| type = Supervised Learning
}}

Example 3: Technical Documentation Wiki Page

Input IPYNB file (research.ipynb):

# Markdown Cell:
# ETL Pipeline Documentation
## Data Sources
Data is collected from three APIs and merged into a warehouse.

# Code Cell:
sources = {
    'UserAPI': {'records': 50000, 'frequency': 'hourly'},
    'OrderAPI': {'records': 125000, 'frequency': 'real-time'},
    'InventoryAPI': {'records': 8000, 'frequency': 'daily'}
}
for name, info in sources.items():
    print(f"{name}: {info['records']} records, {info['frequency']}")

# Output:
UserAPI: 50000 records, hourly
OrderAPI: 125000 records, real-time
InventoryAPI: 8000 records, daily

Output MEDIAWIKI file (research.mediawiki):

== ETL Pipeline Documentation ==

=== Data Sources ===

Data is collected from three APIs and merged into a warehouse.


sources = {
    'UserAPI': {'records': 50000, 'frequency': 'hourly'},
    'OrderAPI': {'records': 125000, 'frequency': 'real-time'},
    'InventoryAPI': {'records': 8000, 'frequency': 'daily'}
}
for name, info in sources.items():
    print(f"{name}: {info['records']} records, {info['frequency']}")


{| class="wikitable"
|-
! Source !! Records !! Frequency
|-
| UserAPI || 50000 || hourly
|-
| OrderAPI || 125000 || real-time
|-
| InventoryAPI || 8000 || daily
|}

Frequently Asked Questions (FAQ)

Q: What is MediaWiki markup?

A: MediaWiki markup (also called wikitext) is the markup language used by MediaWiki software, which powers Wikipedia. It uses distinctive syntax: == for headings, '''bold''', ''italic'', [[internal links]], and {| |} for tables. It is specifically designed for collaborative web-based editing.

Q: How are code cells formatted in MediaWiki?

A: Code cells are converted to blocks or

 blocks in MediaWiki. The syntaxhighlight extension provides syntax coloring if installed on the target wiki, while pre blocks display code in monospace font without highlighting.


            
Q: Can I paste the output directly into Wikipedia?
            
A: The markup is compatible with Wikipedia's syntax. However, Wikipedia has strict content policies (notability, verifiability, neutral point of view) that must be met. The converted content is more typically used for internal corporate wikis, educational wikis, or community knowledge bases.

            
Q: How are notebook images handled?
            
A: Images need to be uploaded separately to the wiki (via Special:Upload) and then referenced in the markup using [[File:imagename.png|thumb|Caption]] syntax. The conversion creates the appropriate file references, but the actual images must be uploaded independently.

            
Q: Will tables from notebook outputs be preserved?
            
A: Yes, data tables are converted to MediaWiki table syntax using {| class="wikitable" |} blocks. This produces properly formatted tables with borders and headers when rendered on any MediaWiki platform.

            
Q: Can I add wiki categories and templates after conversion?
            
A: Yes! After conversion, you can add [[Category:Data Science]] tags, {{infobox}} templates, and other MediaWiki-specific elements to organize and enhance the content within your wiki's structure.

            
Q: Does the conversion support mathematical equations?
            
A: LaTeX math notation from notebook cells is converted to MediaWiki's $tags,\; which\; MediaWiki\; renders\; using\; its\; built-in\; math\; extension.\; Both\; inline\; and\; display\; math\; equations\; are\; supported\; on\; most\; MediaWiki\; installations.$

            
Q: Which MediaWiki versions are supported?
            
A: The output uses standard MediaWiki markup that is compatible with all modern MediaWiki versions (1.25+). The core syntax (headings, bold, italic, links, tables) has been stable since early MediaWiki versions and works across all installations.