Pdit

pdit Collaboration

pdit is an interactive Python editor with inline execution results. The user sees a browser-based split-pane interface: code editor (left) and streaming execution results (right).

User's Environment

Starting pdit:

pdit runs using uv run --with so it's available without modifying the project:

uv run --with git+https://github.com/vangberg/pdit@dist pdit script.py

Note: The @dist branch contains pre-built frontend assets, so no Node.js or build step is needed.

Upgrading pdit:

Since pdit is run with --with, it will fetch the latest version each time. To force a fresh fetch:

uv cache clean pdit

IMPORTANT: Run with run_in_background: true in the Bash tool so you can continue editing.

Options:

• --port 9000 - Use different port (default: 8888)
• --no-browser - Don't auto-open browser

Server runs on localhost:8888, browser opens automatically.

Exporting to HTML:

uv run --with git+https://github.com/vangberg/pdit@dist pdit --export script.py

This executes the script and generates script.html - a self-contained HTML file that can be opened in any browser without a server.

What the user sees:

• Left pane: CodeMirror Python editor with syntax highlighting
• Right pane: Execution results grouped by source lines
• Top bar: RUN CURRENT, RUN ALL, SAVE buttons

User shortcuts:

Code Style

Write simple, top-level exploratory code - like a Jupyter notebook, not a production module.

• Prefer flat, sequential statements over functions and classes
• Each line/expression shows its result inline - take advantage of this
• Avoid unnecessary abstractions, loops, or defensive code
• Focus on data exploration: load, inspect, transform, visualize

import pandas as pd

df = pd.read_csv("data.csv")
df.head()
df.shape
df.describe()
df["price"].mean()
df[df["price"] > 100]

Execution Model

• Stateful: Variables persist across executions (like Jupyter kernel)
• Streaming: Results appear as each statement completes via SSE
• Line-grouped: Results display next to the code that produced them
• Reset: User can reset namespace to clear all variables

Collaboration Workflow

File watching enabled: When you edit the Python file, changes appear in the user's editor automatically.

Validate before the user sees it:

• Run the script yourself with uv run script.py before presenting to user
• Fix any errors or warnings so output is clean
• User should see working code, not debug your mistakes

Recommended workflow:

1. Write/edit the .py file
2. Run uv run script.py to validate - fix any errors
3. User sees clean code and runs with Cmd+Enter

When user shares output:

• Results show stdout, stderr, and expression values
• Errors include full tracebacks
• Results are associated with specific line ranges

API (if needed)

POST /api/execute-script  - Execute code (SSE streaming)
POST /api/reset           - Reset namespace
GET  /api/read-file       - Read file from disk
POST /api/save-file       - Save file to disk
GET  /api/health          - Health check

Markdown Cells

pdit supports markdown cells for documentation and explanations in top-level docstring

"""
# Data Analysis
This section loads and explores the dataset
"""

import pandas as pd
df = pd.read_csv("data.csv")
df.head()

"""
## Summary Statistics
Let's look at the distribution of values
"""

df.describe()

Usage:

• Start with a triple-quoted docstring ("""...""") containing markdown
• Content is rendered as markdown in the output pane
• Supports standard markdown: headers, lists, links, code blocks, etc.
• Great for documenting analysis steps and explaining results

Matplotlib Plots

IMPORTANT: Always wrap matplotlib plotting code in a plt.ioff() context manager:

import matplotlib.pyplot as plt

with plt.ioff():
    plt.plot([1, 2, 3], [1, 4, 9])
    plt.title("My Plot")
    plt.show()  # ✅ Standard matplotlib display

Key points:

• Use with plt.ioff(): to disable interactive mode
• Use standard plt.show() to display plots
• Plots appear inline in the output pane automatically
• The context manager prevents interactive mode issues while capturing plots

Output Suppression

Append ; to any expression to suppress its output (iPython/Jupyter convention):

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 5));  # Suppressed - no <Figure> output
plt.plot([1, 2, 3], [1, 4, 9])
plt.title("My Plot");  # Suppressed - no Text object output
plt.gca()  # Shows the plot

Works with:

• Any expression: expensive_calc();
• Trailing comments: plt.figure(); # setup
• DataFrames, plots, and regular values

Does not affect:

• Markdown cells (explicit documentation)
• print() statements (stdout is always captured)

Output Types & Rich Display

pdit renders different output types with appropriate visualizations:

DataFrames

Pandas and Polars DataFrames render as interactive TanStack tables:

import pandas as pd
df = pd.read_csv("sales.csv")
df  # Interactive table with pagination, sorting, filtering

Rich HTML Display (_repr_html_)

Objects with a _repr_html_() method render as rich HTML. Many libraries support this:

Plotly - Interactive charts:

import plotly.express as px
fig = px.scatter(df, x="price", y="sales")
fig  # Interactive Plotly chart

Great Tables - Formatted tables:

from great_tables import GT
GT(df).fmt_currency("price").fmt_percent("margin")

pandas Styler - Conditional formatting:

df.style.format({"price": "${:.2f}"}).background_gradient(subset=["sales"])

Custom HTML Widgets

Create your own rich displays by implementing _repr_html_():

class MetricCard:
    def __init__(self, title, value, color="#667eea"):
        self.title = title
        self.value = value
        self.color = color

    def _repr_html_(self):
        return f'''
        <div style="background:{self.color}; padding:16px; border-radius:8px; color:white;">
            <div style="font-size:24px; font-weight:bold;">{self.value}</div>
            <div style="opacity:0.8;">{self.title}</div>
        </div>
        '''

MetricCard("Total Revenue", "$1,234,567")  # Renders as styled card

This pattern is powerful for building dashboards and custom visualizations.

Tips

• Expressions show their values inline - print() is rarely needed
• Write intermediate expressions to see values: df.shape instead of print(df.shape)
• Long-running code streams results as each statement completes
• User has full filesystem and package access (local Python execution)
• Use docstring-style markdown cells to document your analysis
• Always wrap matplotlib plotting code in with plt.ioff(): and use plt.show()
• Use ; to suppress unwanted output like plt.figure() return values

Dependencies

Since pdit runs from the local uv environment, add packages with uv add:

uv add pandas matplotlib

No server restart needed - packages are available immediately in both pdit and uv run validation.

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