Skip to content

How it works

Theory

You might think that Narwhals runs on underwater unicorn magic. However, this section exists to reassure you that there's no such thing. There's only one rule you need to understand in order to make sense of Narwhals:

An expression is a function from a DataFrame to a sequence of Series.

For example, nw.col('a') means "given a dataframe df, give me the Series 'a' from df". Translating this to pandas syntax, we get:

def col_a(df):
    return [df.loc[:, 'a']]

Let's step up the complexity. How about nw.col('a')+1? We already know what the nw.col('a') part looks like, so we just need to add 1 to each of its outputs:

def col_a(df):
    return [df.loc[:, 'a']]

def col_a_plus_1(df):
    return [x+1 for x in col_a(df)]

Expressions can return multiple Series - for example, nw.col('a', 'b') translates to:

def col_a_b(df):
    return [df.loc[:, 'a'], df.loc[:, 'b']]

Expressions can also take multiple columns as input - for example, nw.sum_horizontal('a', 'b') translates to:

def sum_horizontal_a_b(df):
    return [df.loc[:, 'a'] + df.loc[:, 'b']]

Note that although an expression may have multiple columns as input, those columns must all have been derived from the same dataframe. This last sentence was quite important, you might want to re-read it to make sure it sunk in.

By itself, an expression doesn't produce a value. It only produces a value once you give it to a DataFrame context. What happens to the value(s) it produces depends on which context you hand it to:

  • DataFrame.select: produce a DataFrame with only the result of the given expression
  • DataFrame.with_columns: produce a DataFrame like the current one, but also with the result of the given expression
  • DataFrame.filter: evaluate the given expression, and if it only returns a single Series, then only keep rows where the result is True.

Now let's turn our attention to the implementation.

Polars implementation

For Polars, Narwhals just "passes everything through". For example consider the following: 

import polars as pl
import narwhals as nw

df_pl = pl.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
df = nw.from_native(df_pl)
df.select(nw.col('a')+1)

nw.col('a') produces a narwhals.expression.Expr object, which has a private _call method. Inside DataFrame.select, we call nw.col('a')._call(pl), which produces pl.col('a').

We then let Polars do its thing. Which is nice, but also not particularly interesting. How about translating expressions to pandas? Well, it's interesting to us, and you're still reading, so maybe it'll be interesting to you too.

pandas implementation

When we called nw.col('a')._call(pl), we got a Narwhals-compliant Polars namespace. The pandas namespace (pd) isn't Narwhals-compliant, as the pandas API is very different from Polars'. So...Narwhals implements a PandasNamespace, which includes the top-level Polars functions included in the Narwhals API:

import narwhals as nw
from narwhals._pandas_like.namespace import PandasNamespace

pn = PandasNamespace(implementation='pandas')
nw.col('a')._call(pn)
The result from the last line above is the same as we'd get from pn.col('a'), and it's a narwhals._pandas_like.expression.PandasExpr object, which we'll call PandasExpr for short.

PandasExpr also has a _call method - but this one expects a PandasDataFrame as input. Recall from above that an expression is a function from a dataframe to a sequence of series. The _call method gives us that function! Let's see it in action.

Note: the following examples use PandasDataFrame and PandasSeries. These are backed by actual pandas.DataFrames and pandas.Series respectively and are Narwhals-compliant. We can access the underlying pandas objects via PandasDataFrame._dataframe and PandasSeries._series.

import narwhals as nw
from narwhals._pandas_like.namespace import PandasNamespace
from narwhals._pandas_like.dataframe import PandasDataFrame
import pandas as pd

pn = PandasNamespace(implementation='pandas')

df_pd = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
df = PandasDataFrame(df_pd, implementation='pandas')
expression = pn.col('a') + 1
result = expression._call(df)
print([x._series for x in result])
The first (and only) Series to be output is: 
0    2
1    3
2    4
Name: a, dtype: int64

So indeed, our expression did what it said on the tin - it took some dataframe, took column 'a', and added 1 to it.

If you search for def reuse_series_implementation, you'll see that that's all expressions do in Narwhals - they just keep rigorously applying the definition of expression.

It may look like there should be significant overhead to doing it this way - but really, it's just a few Python calls which get unwinded. From timing tests I've done, there's no detectable difference - in fact, because the Narwhals API guards against misusing the pandas API, it's likely that running pandas via Narwhals will in general be more efficient than running pandas directly.

Further attempts at demistifying Narwhals, refactoring code so it's clearer, and explaining this section better are 110% welcome.

Group-by

Group-by is probably one of Polars' most significant innovations (on the syntax side) with respect to pandas. We can write something like 

df: pl.DataFrame
df.group_by('a').agg((pl.col('c') > pl.col('b').mean()).max())
To do this in pandas, we need to either use GroupBy.apply (sloooow), or do some crazy manual optimisations to get it to work.

In Narwhals, here's what we do:

  • if somebody uses a simple group-by aggregation (e.g. df.group_by('a').agg(nw.col('b').mean())), then on the pandas side we translate it to 
    df: pd.DataFrame
df.groupby('a').agg({'b': ['mean']})
  • if somebody passes a complex group-by aggregation, then we use apply and raise a UserWarning, warning users of the performance penalty and advising them to refactor their code so that the aggregation they perform ends up being a simple one.

In order to tell whether an aggregation is simple, Narwhals uses the private _depth attribute of PandasExpr:

>>> pn.col('a').mean()
PandasExpr(depth=1, function_name=col->mean, root_names=['a'], output_names=['a']
>>> (pn.col('a')+1).mean()
PandasExpr(depth=2, function_name=col->__add__->mean, root_names=['a'], output_names=['a']
>>> pn.mean('a')
PandasExpr(depth=1, function_name=col->mean, root_names=['a'], output_names=['a']

For simple aggregations, Narwhals can just look at _depth and function_name and figure out which (efficient) elementary operation this corresponds to in pandas.