Tired of getting lost in if-then statements when dealing with API differences between dataframe libraries? Would you like to be able to write your code once, have it work with all major dataframe libraries, and be done? Let’s learn about an initiative which will enable you to write cross-dataframe code - no special-casing nor data conversions required!
Why would I want this anyway?
Say you want to write a function which selects rows of a dataframe based on the z-score of a given column, and you want it to work with any dataframe library. How might you write that?
Here’s a typical solution:
def remove_outliers(df: object, column: str) -> pd.DataFrame: if isinstance(df, pandas.DataFrame): z_score = (df[column] - df[column].mean())/df[column].std() return df[z_score.between(-3, 3)] if isinstance(df, polars.DataFrame): z_score = ((pl.col(column) - pl.col(column).mean()) / pl.col(column).std()) return df.filter(z_score.is_between(-3, 3)) if isinstance(df, some_other_library.DataFrame): ...
This quickly gets unwieldy. Libraries like
modin might work
isinstance(df, pandas.DataFrame) arm, but there’s no guarantee -
their APIs are similar, but subtly different. Furthermore, as new libraries
come out, you’d have to keep updating your function to add new
Can we do better?
Solution 2: Interchange Protocol
An alternative, which wouldn’t involve special-casing, could be to leverage the DataFrame interchange protocol:
def remove_outliers(df: object, column: str) -> pd.DataFrame: df_pd = pd.api.interchange.from_dataframe(df) z_score = (df_pd[column] - df_pd[column].mean())/df_pd[column].std() return df_pd[z_score.between(-3, 3)]
We got out of having to write if-then statements (🥳), but there’s still a couple of issues:
- we had to convert to pandas: this might be expensive if your data was originally stored on GPU;
- the return value is a
pandas.DataFrame, rather than an object of your original dataframe library.
Can we do better? Can we really have it all?
Solution 3: Introducing the Dataframe Standard
Yes, we really can. To write cross-dataframe code, we’ll take these steps:
- enable the Standard using
.__dataframe_standard__. This will return a Standard-compliant dataframe;
- write your code, using the Dataframe Standard specification
- (optional) return a dataframe from your original library by calling
Let’s see how this would look like for our
remove_outliers example function:
def remove_outliers(df, column): # Get a Standard-compliant dataframe. # NOTE: this has not yet been upstreamed, so won't work out-of-the-box! # See 'resources' below for how to try it out. df_standard = df.__dataframe_standard__() # Use methods from the Standard specification. col = df_standard.get_column_by_name(column) z_score = (col - col.mean()) / col.std() df_standard_filtered = df_standard.get_rows_by_mask((z_score > -3) & (z_score < 3)) # Return the result as a dataframe from the original library. return df_standard_filtered.dataframe
This will work, as if by magic, on any dataframe with a Standard-compliant implementation. But it’s not magic, of course, it’s the power of standardisation!
The Standard’s philosophy - will all dataframe libraries have the same API one day?
Let’s start with what this isn’t: the Standard isn’t an attempt to force all dataframe libraries to have the same API. It also isn’t a way to convert between dataframes: the Interchange Protocol, whose adoption is increasing, already does that. It also doesn’t aim to standardise domain or industry specific functionality.
Rather, it is minimal set of essential dataframe functionality which will work the same way across libraries. It will behave in a strict and predictable manner across dataframe libraries. Library authors trying to write dataframe-agnostic code are expected to greatly benefit from this, as are their users.
Who’s this for? Do I need to learn yet another API?
If you’re a casual user, then probably not. The Dataframe Standard is currently mainly targeted towards library developers, who wish to support multiple dataframe libraries. Users of non-pandas dataframe libraries would then be able to seamlessly use the Python packages which provide functionality for dataframes (e.g. visualisation, feature engineering, data cleaning) without having to do any expensive data conversions.
If you’re a library author, then we’d love to hear from you. Would this be useful to you? We expect it to be, as the demand for dataframe-agnostic tools certainly seems to be there:
- (many, many more…)
Are we there yet? What lies ahead?
This is just a first draft, based on design discussions between authors from various dataframe libraries, and a request for comments (RFC). Our goal is to solicit input from a wider range of potential stakeholders, and evolve the Standard throughout the rest of 2023, resulting in a first official release towards the end of the year.
Future plans include:
- increasing the scope of the Standard based on real-world code from widely used packages (currently, the spec is very minimal);
- creating implementations of the Standard for several major dataframe libraries
(initially available as a separate
- creating a cross-dataframe test-suite;
- aiming to ensure each major dataframe library has a
We’ve introduced the Dataframe Standard, which allows you to write cross-dataframe code. We learned about its philosophy, as well as what it doesn’t aim to be. Finally, we saw what plans lie ahead - the Standard is in active development, so please watch this space!