pandas

Ibis’s pandas backend is available in core Ibis:

Create a client by supplying a dictionary of DataFrames using ibis.pandas.connect(). The keys become the table names:

import pandas as pd
con = ibis.pandas.connect(
    {
       'A': pd._testing.makeDataFrame(),
       'B': pd._testing.makeDataFrame(),
    }
)

User Defined functions (UDF)

Ibis supports defining three kinds of user-defined functions for operations on expressions targeting the pandas backend: element-wise, reduction, and analytic.

Element-wise Functions

An element-wise function is a function that takes N rows as input and produces N rows of output. log, exp, and floor are examples of element-wise functions.

Here’s how to define an element-wise function:

import ibis.expr.datatypes as dt
from ibis.backends.pandas import udf

@udf.elementwise(input_type=[dt.int64], output_type=dt.double)
def add_one(x):
    return x + 1.0

Reduction Functions

A reduction is a function that takes N rows as input and produces 1 row as output. sum, mean and count are examples of reductions. In the context of a GROUP BY, reductions produce 1 row of output per group.

Here’s how to define a reduction function:

import ibis.expr.datatypes as dt
from ibis.backends.pandas import udf

@udf.reduction(input_type=[dt.double], output_type=dt.double)
def double_mean(series):
    return 2 * series.mean()

Analytic Functions

An analytic function is like an element-wise function in that it takes N rows as input and produces N rows of output. The key difference is that analytic functions can be applied per group using window functions. Z-score is an example of an analytic function.

Here’s how to define an analytic function:

import ibis.expr.datatypes as dt
from ibis.backends.pandas import udf

@udf.analytic(input_type=[dt.double], output_type=dt.double)
def zscore(series):
    return (series - series.mean()) / series.std()

Details of Pandas UDFs

  • Element-wise functions automatically provide support for applying your UDF to any combination of scalar values and columns.

  • Reduction functions automatically provide support for whole column aggregations, grouped aggregations, and application of your function over a window.

  • Analytic functions work in both grouped and non-grouped settings

  • The objects you receive as input arguments are either pandas.Series or Python/NumPy scalars.

Note

Any keyword arguments must be given a default value or the function will not work.

A common Python convention is to set the default value to None and handle setting it to something not None in the body of the function.

Using add_one from above as an example, the following call will receive a pandas.Series for the x argument:

import ibis
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3]})
con = ibis.backends.pandas.connect({'df': df})
t = con.table('df')
expr = add_one(t.a)
expr

And this will receive the int 1:

expr = add_one(1)
expr

Since the pandas backend passes around **kwargs you can accept **kwargs in your function:

import ibis.expr.datatypes as dt
from ibis.backends.pandas import udf

@udf.elementwise([dt.int64], dt.double)
def add_two(x, **kwargs):
    # do stuff with kwargs
    return x + 2.0

Or you can leave them out as we did in the example above. You can also optionally accept specific keyword arguments.

For example:

import ibis.expr.datatypes as dt
from ibis.backends.pandas import udf

@udf.elementwise([dt.int64], dt.double)
def add_two_with_none(x, y=None):
    if y is None:
        y = 2.0
    return x + y