ptmcg/pyscript_littletable_iris.html

## pyscript_littletable_iris.html
<html>
    <head>
    <script defer src="https://pyscript.net/alpha/pyscript.min.js"></script>
    <py-env>
        - littletable
    </py-env>
    </head>
<body>

<h2>Iris data</h2>

<py-script>
from pyodide.http import open_url
import littletable as lt


url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"

# define column names and numeric conversion transforms
names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "class"]
iris_transforms = dict.fromkeys(
    ["petal-length", "petal-width", "sepal-length", "sepal-width"], lt.Table.convert_numeric
)

# import the data from remote URL
iris_table = lt.Table(url).csv_import(
    open_url(url), fieldnames=names, transforms=iris_transforms
)
iris_table.create_index("class")

print(iris_table.table_name)

# print summary counts by class name
print(iris_table.pivot("class")
      .as_table()
      .as_html(table_properties={"border": 1, "cellpadding": 2}))
</py-script>

<p>
<h2>Iris summary stats</h2>

<py-script>
# compute and print summary statistics for numeric columns
stats = iris_table.stats(
    ["petal-length", "petal-width", "sepal-length", "sepal-width"]
)
print(stats("stats").as_html(table_properties={"border": 1, "cellpadding": 2}))
</py-script>

    </body>
</html>
	<html>
	<head>
	<script defer src="https://pyscript.net/alpha/pyscript.min.js"></script>
	<py-env>
	- littletable
	</py-env>
	</head>
	<body>

	<h2>Iris data</h2>

	<py-script>
	from pyodide.http import open_url
	import littletable as lt


	url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"

	# define column names and numeric conversion transforms
	names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "class"]
	iris_transforms = dict.fromkeys(
	["petal-length", "petal-width", "sepal-length", "sepal-width"], lt.Table.convert_numeric
	)

	# import the data from remote URL
	iris_table = lt.Table(url).csv_import(
	open_url(url), fieldnames=names, transforms=iris_transforms
	)
	iris_table.create_index("class")

	print(iris_table.table_name)

	# print summary counts by class name
	print(iris_table.pivot("class")
	.as_table()
	.as_html(table_properties={"border": 1, "cellpadding": 2}))
	</py-script>

	<p>
	<h2>Iris summary stats</h2>

	<py-script>
	# compute and print summary statistics for numeric columns
	stats = iris_table.stats(
	["petal-length", "petal-width", "sepal-length", "sepal-width"]
	)
	print(stats("stats").as_html(table_properties={"border": 1, "cellpadding": 2}))
	</py-script>

	</body>
	</html>