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Update app.py
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app.py
CHANGED
@@ -1,470 +1,145 @@
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import marimo
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__generated_with = "0.
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app = marimo.App()
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@app.cell
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def
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import marimo as mo
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@app.cell
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def
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@app.cell
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def
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marimo is a **reactive** Python notebook.
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This means that unlike traditional notebooks, marimo notebooks **run
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automatically** when you modify them or
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interact with UI elements, like this slider: {slider}.
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)
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return
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@app.cell
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def
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"Tip: disabling automatic execution": mo.md(
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rf"""
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marimo lets you disable automatic execution: just go into the
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notebook settings and set
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"Runtime > On Cell Change" to "lazy".
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When the runtime is lazy, after running a cell, marimo marks its
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descendants as stale instead of automatically running them. The
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lazy runtime puts you in control over when cells are run, while
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still giving guarantees about the notebook state.
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"""
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)
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}
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)
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return
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@app.cell
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def
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"""
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Tip: This is a tutorial notebook. You can create your own notebooks
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by entering `marimo edit` at the command line.
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"""
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).callout()
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return
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@app.cell
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def
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"""
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## 1. Reactive execution
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A marimo notebook is made up of small blocks of Python code called
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cells.
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marimo reads your cells and models the dependencies among them: whenever
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a cell that defines a global variable is run, marimo
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**automatically runs** all cells that reference that variable.
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Reactivity keeps your program state and outputs in sync with your code,
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making for a dynamic programming environment that prevents bugs before they
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happen.
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"""
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)
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return
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mo.md(
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f"""
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**β¨ Nice!** The value of `changed` is now {changed}.
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When you updated the value of the variable `changed`, marimo
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**reacted** by running this cell automatically, because this cell
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references the global variable `changed`.
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Reactivity ensures that your notebook state is always
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consistent, which is crucial for doing good science; it's also what
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enables marimo notebooks to double as tools and apps.
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"""
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)
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if changed
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else mo.md(
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"""
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**π See it in action.** In the next cell, change the value of the
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variable `changed` to `True`, then click the run button.
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"""
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)
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)
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return
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@app.cell
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def
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@app.cell(hide_code=True)
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def __(mo):
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mo.accordion(
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{
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"Tip: execution order": (
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"""
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The order of cells on the page has no bearing on
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the order in which cells are executed: marimo knows that a cell
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reading a variable must run after the cell that defines it. This
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frees you to organize your code in the way that makes the most
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sense for you.
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"""
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)
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}
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)
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return
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@app.cell
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def
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return
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@app.cell
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def
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{
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"Tip: encapsulation": (
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"""
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By encapsulating logic in functions, classes, or Python modules,
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you can minimize the number of global variables in your notebook.
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"""
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)
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}
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)
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return
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@app.cell
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def
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{
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"Tip: private variables": (
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"""
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Variables prefixed with an underscore are "private" to a cell, so
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they can be defined by multiple cells.
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"""
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)
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}
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)
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return
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@app.cell(hide_code=True)
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def
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mo.md(
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"""
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## 2. UI elements
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Cells can output interactive UI elements. Interacting with a UI
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element **automatically triggers notebook execution**: when
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you interact with a UI element, its value is sent back to Python, and
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every cell that references that element is re-run.
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marimo provides a library of UI elements to choose from under
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`marimo.ui`.
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"""
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)
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return
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@app.cell
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def
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return
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@app.cell
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def
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return (icon,)
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@app.cell
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def __(icon, mo):
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repetitions = mo.ui.slider(1, 16, label=f"number of {icon.value}: ")
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return (repetitions,)
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@app.cell
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def __(icon, repetitions):
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icon, repetitions
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return
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@app.cell
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def
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 3. marimo is just Python
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marimo cells parse Python (and only Python), and marimo notebooks are
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stored as pure Python files β outputs are _not_ included. There's no
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magical syntax.
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The Python files generated by marimo are:
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- easily versioned with git, yielding minimal diffs
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- legible for both humans and machines
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- formattable using your tool of choice,
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- usable as Python scripts, with UI elements taking their default
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values, and
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- importable by other modules (more on that in the future).
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 4. Running notebooks as apps
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marimo notebooks can double as apps. Click the app window icon in the
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bottom-right to see this notebook in "app view."
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Serve a notebook as an app with `marimo run` at the command-line.
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Of course, you can use marimo just to level-up your
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notebooking, without ever making apps.
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 5. The `marimo` command-line tool
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**Creating and editing notebooks.** Use
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```
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marimo edit
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```
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in a terminal to start the marimo notebook server. From here
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you can create a new notebook or edit existing ones.
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**Running as apps.** Use
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```
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marimo run notebook.py
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```
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to start a webserver that serves your notebook as an app in read-only mode,
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with code cells hidden.
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**Convert a Jupyter notebook.** Convert a Jupyter notebook to a marimo
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notebook using `marimo convert`:
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```
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marimo convert your_notebook.ipynb > your_app.py
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```
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**Tutorials.** marimo comes packaged with tutorials:
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- `dataflow`: more on marimo's automatic execution
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- `ui`: how to use UI elements
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- `markdown`: how to write markdown, with interpolated values and
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LaTeX
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- `plots`: how plotting works in marimo
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- `sql`: how to use SQL
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- `layout`: layout elements in marimo
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- `fileformat`: how marimo's file format works
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- `markdown-format`: for using `.md` files in marimo
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- `for-jupyter-users`: if you are coming from Jupyter
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Start a tutorial with `marimo tutorial`; for example,
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```
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marimo tutorial dataflow
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```
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In addition to tutorials, we have examples in our
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[our GitHub repo](https://www.github.com/marimo-team/marimo/tree/main/examples).
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 6. The marimo editor
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Here are some tips to help you get started with the marimo editor.
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"""
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)
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return
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@app.cell
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def
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mo.accordion(tips)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md("""## Finally, a fun fact""")
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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The name "marimo" is a reference to a type of algae that, under
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the right conditions, clumps together to form a small sphere
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called a "marimo moss ball". Made of just strands of algae, these
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beloved assemblages are greater than the sum of their parts.
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"""
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)
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return
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@app.cell(hide_code=True)
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def __():
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tips = {
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"Saving": (
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"""
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**Saving**
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- _Name_ your app using the box at the top of the screen, or
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with `Ctrl/Cmd+s`. You can also create a named app at the
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command line, e.g., `marimo edit app_name.py`.
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- _Save_ by clicking the save icon on the bottom right, or by
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inputting `Ctrl/Cmd+s`. By default marimo is configured
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to autosave.
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"""
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),
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"Running": (
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"""
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1. _Run a cell_ by clicking the play ( β· ) button on the top
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right of a cell, or by inputting `Ctrl/Cmd+Enter`.
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2. _Run a stale cell_ by clicking the yellow run button on the
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right of the cell, or by inputting `Ctrl/Cmd+Enter`. A cell is
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stale when its code has been modified but not run.
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3. _Run all stale cells_ by clicking the play ( β· ) button on
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the bottom right of the screen, or input `Ctrl/Cmd+Shift+r`.
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"""
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),
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"Console Output": (
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"""
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Console output (e.g., `print()` statements) is shown below a
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cell.
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"""
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),
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"Creating, Moving, and Deleting Cells": (
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"""
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1. _Create_ a new cell above or below a given one by clicking
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the plus button to the left of the cell, which appears on
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mouse hover.
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2. _Move_ a cell up or down by dragging on the handle to the
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right of the cell, which appears on mouse hover.
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3. _Delete_ a cell by clicking the trash bin icon. Bring it
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back by clicking the undo button on the bottom right of the
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screen, or with `Ctrl/Cmd+Shift+z`.
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"""
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),
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"Disabling Automatic Execution": (
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"""
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Via the notebook settings (gear icon) or footer panel, you
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can disable automatic execution. This is helpful when
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working with expensive notebooks or notebooks that have
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side-effects like database transactions.
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"""
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),
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"Disabling Cells": (
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"""
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You can disable a cell via the cell context menu.
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marimo will never run a disabled cell or any cells that depend on it.
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This can help prevent accidental execution of expensive computations
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when editing a notebook.
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"""
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),
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"Code Folding": (
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"""
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You can collapse or fold the code in a cell by clicking the arrow
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icons in the line number column to the left, or by using keyboard
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shortcuts.
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Use the command palette (`Ctrl/Cmd+k`) or a keyboard shortcut to
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quickly fold or unfold all cells.
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"""
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),
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"Code Formatting": (
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"""
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If you have [ruff](https://github.com/astral-sh/ruff) installed,
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you can format a cell with the keyboard shortcut `Ctrl/Cmd+b`.
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"""
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),
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"Command Palette": (
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"""
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Use `Ctrl/Cmd+k` to open the command palette.
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"""
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),
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"Keyboard Shortcuts": (
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"""
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Open the notebook menu (top-right) or input `Ctrl/Cmd+Shift+h` to
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view a list of all keyboard shortcuts.
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"""
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),
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"Configuration": (
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"""
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Configure the editor by clicking the gears icon near the top-right
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of the screen.
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"""
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),
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}
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return (tips,)
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if __name__ == "__main__":
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app.run()
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# /// script
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# requires-python = ">=3.13"
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# dependencies = [
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# "marimo",
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# "polars==1.29.0",
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# "pyarrow==20.0.0",
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# "pyiceberg==0.9.1",
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# "sqlalchemy==2.0.40",
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# ]
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# ///
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import marimo
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__generated_with = "0.13.7"
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app = marimo.App(width="full")
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@app.cell
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def _():
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import marimo as mo
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import sqlalchemy
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import polars as pl
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from pathlib import Path
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from pyiceberg.partitioning import PartitionSpec, PartitionField
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from pyiceberg.transforms import IdentityTransform
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return IdentityTransform, PartitionField, PartitionSpec, mo, pl
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@app.cell
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def _():
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+
from pyiceberg.catalog import load_catalog
|
32 |
+
|
33 |
+
warehouse_path = "warehouse"
|
34 |
+
catalog = load_catalog(
|
35 |
+
"default",
|
36 |
+
**{
|
37 |
+
'type': 'sql',
|
38 |
+
"uri": f"sqlite:///{warehouse_path}/iceberg.db",
|
39 |
+
"warehouse": f"file://{warehouse_path}",
|
40 |
+
},
|
41 |
+
)
|
42 |
+
return (catalog,)
|
43 |
|
44 |
|
45 |
@app.cell
|
46 |
+
def _(pl):
|
47 |
+
df_taxi = pl.read_csv("yellow_tripdata_2015-01.csv").to_arrow()
|
48 |
+
return (df_taxi,)
|
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|
49 |
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|
50 |
|
51 |
+
@app.cell
|
52 |
+
def _(df_taxi):
|
53 |
+
df_taxi.group_by("passenger_count").aggregate([([], "count_all")])
|
54 |
return
|
55 |
|
56 |
|
57 |
+
@app.cell
|
58 |
+
def _(IdentityTransform, PartitionField, PartitionSpec):
|
59 |
+
spec = PartitionSpec(
|
60 |
+
PartitionField(source_id=3, field_id=1000, name="passenger_count", transform=IdentityTransform())
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|
61 |
)
|
62 |
return
|
63 |
|
64 |
|
65 |
+
@app.cell
|
66 |
+
def _(df_taxi):
|
67 |
+
df_taxi.schema
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|
68 |
return
|
69 |
|
70 |
|
71 |
+
@app.cell
|
72 |
+
def _(catalog, df_taxi):
|
73 |
+
catalog.create_namespace_if_not_exists("default")
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|
74 |
|
75 |
+
table = catalog.create_table_if_not_exists(
|
76 |
+
"default.taxi",
|
77 |
+
schema=df_taxi.schema,
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|
78 |
)
|
79 |
+
return (table,)
|
80 |
|
81 |
|
82 |
@app.cell
|
83 |
+
def _(df_taxi, table):
|
84 |
+
if not table.current_snapshot():
|
85 |
+
table.append(df_taxi)
|
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|
86 |
return
|
87 |
|
88 |
|
89 |
+
@app.cell
|
90 |
+
def _(catalog):
|
91 |
+
(
|
92 |
+
catalog
|
93 |
+
.load_table("default.taxi")
|
94 |
+
.to_polars()
|
95 |
+
.group_by("passenger_count")
|
96 |
+
.len()
|
97 |
+
.sort("passenger_count")
|
98 |
+
.collect()
|
99 |
)
|
100 |
return
|
101 |
|
102 |
|
103 |
+
@app.cell
|
104 |
+
def _(pl):
|
105 |
+
pl.scan_csv("yellow_tripdata_2015-01.csv").group_by("passenger_count").len().sort("passenger_count").collect()
|
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|
106 |
return
|
107 |
|
108 |
|
109 |
+
@app.cell
|
110 |
+
def _(pl):
|
111 |
+
pl.read_csv("yellow_tripdata_2015-01.csv").group_by("passenger_count").len().sort("passenger_count")
|
|
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|
|
112 |
return
|
113 |
|
114 |
|
115 |
@app.cell(hide_code=True)
|
116 |
+
def _(mo):
|
117 |
+
mo.md(r"""The partition is great, but the comparison with `read_csv` is a bit unfair. Let's convert the `.csv` file to `.parquet` and also add a partition in polars with statistics. """)
|
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|
118 |
return
|
119 |
|
120 |
|
121 |
@app.cell
|
122 |
+
def _(pl):
|
123 |
+
pl.read_csv("yellow_tripdata_2015-01.csv").write_parquet("taxi.parquet", partition_by=["passenger_count"], statistics=True)
|
124 |
return
|
125 |
|
126 |
|
127 |
@app.cell
|
128 |
+
def _(pl):
|
129 |
+
pl.scan_parquet("taxi.parquet").group_by("passenger_count").len().sort("passenger_count").collect()
|
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|
130 |
return
|
131 |
|
132 |
|
133 |
@app.cell
|
134 |
+
def _(pl):
|
135 |
+
pl.read_parquet("taxi.parquet").group_by("passenger_count").len().sort("passenger_count")
|
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|
136 |
return
|
137 |
|
138 |
|
139 |
@app.cell
|
140 |
+
def _():
|
|
|
141 |
return
|
142 |
|
143 |
|
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|
144 |
if __name__ == "__main__":
|
145 |
app.run()
|