🎨 Misc examples

🎨 Simple example

from typeddfs import TypedDfs

MyDfType = (
    TypedDfs.typed("MyDfType")
    .require("name", index=True)  # always keep in index
    .require("value", dtype=float)  # require a column and type
    .drop("_temp")  # auto-drop a column
    .verify(lambda ddf: len(ddf) == 12)  # require exactly 12 rows
).build()

df = MyDfType.read_file(input("filename? [.feather/.csv.gz/.tsv.xz/etc.]"))
df = df.sort_natural()
df.write_file("myfile.feather", mkdirs=True)
# want to write to a sha1sum-like (.sha256) file?
df.write_file("myfile.feather", file_hash=True)
# verify it?
MyDfType.read_file("myfile.feather", check_hash="file")

📉 A matrix-style DataFrame

import numpy as np
from typeddfs import TypedDfs

Symmetric64 = (
    TypedDfs.matrix("Symmetric64", doc="A symmetric float64 matrix")
    .dtype(np.float64)
    .verify(lambda df: df.values.sum().sum() == 1.0)
    .add_methods(product=lambda df: df.flatten().product())
).build()

mx = Symmetric64.read_file("input.tab")
print(mx.product())  # defined above
if mx.is_symmetric():
    mx = mx.triangle()  # it's symmetric, so we only need half
    long = mx.drop_na().long_form()  # columns: "row", 'column", and "value"
    long.write_file("long-form.xml")

🔍 Example in terms of CSV

For a CSV like this:

key,value,note
abc,123,?

from typeddfs import TypedDfs

# Build me a Key-Value-Note class!
KeyValue = (
    TypedDfs.typed("KeyValue")  # With enforced reqs / typing
    .require("key", dtype=str, index=True)  # automagically add to index
    .require("value")  # required
    .reserve("note")  # permitted but not required
    .strict()  # disallow other columns
).build()

# This will self-organize and use "key" as the index:
df = KeyValue.read_csv("example.csv")

# For fun, let"s write it and read it back:
df.to_csv("remake.csv")
df = KeyValue.read_csv("remake.csv")
print(df.index_names(), df.column_names())  # ["key"], ["value", "note"]

# And now, we can type a function to require a KeyValue,
# and let it raise an `InvalidDfError` (here, a `MissingColumnError`):
def my_special_function(df: KeyValue) -> float:
    return KeyValue(df)["value"].sum()