typeddfs 0.2.0

Pandas DataFrame subclasses that enforce structure and can self-organize.

Typed DataFrames

Pandas DataFrame subclasses that enforce structure and can self-organize. Because your functions can’t exactly accept any DataFrame.

The subclassed DataFrames can have required and/or optional columns and indices, and support custom requirements. Columns are automatically turned into indices, which means read_csv and to_csv are always inverses. MyDf.read_csv(mydf.to_csv()) is just mydf.

The DataFrames will display nicely in Jupyter notebooks, and few convenience methods are added, such as sort_natural and drop_cols. See the docs for more information.

Simple example 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')   # typed means enforced requirements
    .require('key', index=True)  # automagically make this an index
    .require('value')            # required
    .reserve('note')             # permitted but not required
    .strict()                    # don't allow 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('remke.csv')
df = KeyValue('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()

All of the normal DataFrame methods are available. Use .untyped() or .vanilla() to make a detyped copy that doesn't enforce requirements.

New issues and pull requests are welcome. Please refer to the contributing guide. Generated with Tyrannosaurus: tyrannosaurus new typed-dfs.