Your notebooks,
as plain text.

# %% [markdown]
# # Quarterly Sales
# A look at Q4 revenue by region.

# %%
import pandas as pd
df = pd.read_csv("sales.csv")

# %%
df.groupby("region")["revenue"].sum().plot.bar()

Explicit # %% cell markers — understood by VS Code, PyCharm, Spyder, supported in 30+ languages.

# # Quarterly Sales
# A look at Q4 revenue by region.

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

df.groupby("region")["revenue"].sum().plot.bar()

Minimal markers — barely looks different from a regular script. Use py:nomarker to drop them entirely.

import marimo

app = marimo.App()


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # Quarterly Sales
    A look at Q4 revenue by region.
    """)
    return


@app.cell
def _():
    import pandas as pd
    df = pd.read_csv("sales.csv")
    return (df,)


@app.cell
def _(df):
    df.groupby("region")["revenue"].sum().plot.bar()
    return


@app.cell
def _():
    import marimo as mo
    return (mo,)


if __name__ == "__main__":
    app.run()

Each cell becomes a reactive function — open the file directly in Marimo.

# Quarterly Sales
A look at Q4 revenue by region.

```python
import pandas as pd
df = pd.read_csv("sales.csv")
```

```python
df.groupby("region")["revenue"].sum().plot.bar()
```

Plain GitHub Markdown — prose-first, readable everywhere.

# Quarterly Sales
A look at Q4 revenue by region.

```{code-cell}
import pandas as pd
df = pd.read_csv("sales.csv")
```

```{code-cell}
df.groupby("region")["revenue"].sum().plot.bar()
```

Rich directives and cross-references for Jupyter Book and technical publishing.

---
title: Quarterly Sales
jupyter: python3
---

A look at Q4 revenue by region.


```{python}
import pandas as pd
df = pd.read_csv("sales.csv")
```

```{python}
df.groupby("region")["revenue"].sum().plot.bar()
```

Multi-language scientific publishing — Python, R, Julia and more via the Quarto system.

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-0001",
   "metadata": {},
   "source": [
    "# Quarterly Sales\n",
    "A look at Q4 revenue by region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-0002",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-0003",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"region\")[\"revenue\"].sum().plot.bar()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "notebook_metadata_filter": "-all"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

JSON format · contains outputs and metadata, but not well suited to manual edits or version control