https://github.com/machow/coffee-sales-data

https://github.com/machow/coffee-sales-data

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/machow/coffee-sales-data
• Owner: machow
• License: mit
• Created: 2024-05-13T21:20:59.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-16T19:48:05.000Z (8 months ago)
• Last Synced: 2024-05-16T20:59:59.500Z (8 months ago)
• Language: Jupyter Notebook
• Size: 936 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Coffee sales data

This repository generates sales data for a fictitious coffee device

store.

Here are some useful pieces:

- [example_notebook.ipynb](./example_notebook.ipynb): jupyter notebook

  example.

- [README.qmd](./README.qmd): the [quarto](https://quarto.org) document

  which generated this page.

- [Great Tables](https://github.com/posit-dev/great-tables): python

  library making the beautiful tables.

## What can I do with it?

This repo is used to generate this table for the Great Tables examples

gallery (scroll down for code):

![An image of a Great Table output, which is an HTML table. Each row is

a coffee product](./data/coffee-table.png)

## Code for table

``` python

import polars as pl

import polars.selectors as cs

from great_tables import GT, loc, style

coffee_sales = pl.read_json("data/coffee-sales.json")

```

``` python

sel_rev = cs.starts_with("revenue")

sel_prof = cs.starts_with("profit")

coffee_table = (

    GT(coffee_sales)

    .tab_header("Sales of Coffee Equipment")

    .tab_spanner(label="Revenue", columns=sel_rev)

    .tab_spanner(label="Profit", columns=sel_prof)

    .cols_label(

        revenue_dollars="Amount",

        profit_dollars="Amount",

        revenue_pct="Percent",

        profit_pct="Percent",

        monthly_sales="Monthly Sales",

        icon="",

        product="Product",

    )

    # formatting ----

    .fmt_number(

        columns=cs.ends_with("dollars"),

        compact=True,

        pattern="${x}",

        n_sigfig=3,

    )

    .fmt_percent(columns=cs.ends_with("pct"), decimals=0)

    # style ----

    .tab_style(

        style=style.fill(color="aliceblue"),

        locations=loc.body(columns=sel_rev),

    )

    .tab_style(

        style=style.fill(color="papayawhip"),

        locations=loc.body(columns=sel_prof),

    )

    .tab_style(

        style=style.text(weight="bold"),

        locations=loc.body(rows=pl.col("product") == "Total"),

    )

    .fmt_nanoplot("monthly_sales", plot_type="bar")

    .fmt_image("icon", path="assets")

    .sub_missing(missing_text="")

)

coffee_table.save("data/coffee-table.png",  scale=2)

```

See this [example_notebook.ipynb](./example_notebook.ipynb) for this

code in jupyter notebook form.

## Regenerating data and README

``` bash

# generate data ----

python 1-generate.py

# generate README.md ----

quarto render README.qmd

# or to do everything in one command

make all

```