refactor(pandas): remove the pandas backend

.github/workflows/ibis-backends.yml

@@ -117,10 +117,6 @@ jobs:
             extras:
               - clickhouse
               - examples
-          - name: pandas
-            title: Pandas
-            extras:
-              - pandas
           - name: sqlite
             title: SQLite
             extras:

docs/backends/_utils.py

@@ -17,10 +17,7 @@ def get_renderer(level: int) -> MdRenderer:
 
 @cache
 def get_backend(backend: str):
-    if backend == "pandas":
-        return get_object(f"ibis.backends.{backend}", "BasePandasBackend")
-    else:
-        return get_object(f"ibis.backends.{backend}", "Backend")
+    return get_object(f"ibis.backends.{backend}", "Backend")
 
 
 def get_callable(obj, name):

docs/backends/pandas.qmd

@@ -1,213 +1,7 @@
 # pandas
 
-[https://pandas.pydata.org/](https://pandas.pydata.org/)
-
-![](https://img.shields.io/badge/memtables-native-green?style=flat-square) ![](https://img.shields.io/badge/inputs-CSV | Parquet-blue?style=flat-square) ![](https://img.shields.io/badge/outputs-CSV | pandas | Parquet | PyArrow-orange?style=flat-square)
-
-::: {.callout-warning}
-## The Pandas backend is slated for removal in Ibis 10.0
-We recommend using one of our other backends.
-
-Many workloads work well on the DuckDB and Polars backends, for example.
-:::
-
-
-## Install
-
-Install Ibis and dependencies for the pandas backend:
-
-::: {.panel-tabset}
-
-## `pip`
-
-Install with the `pandas` extra:
-
-```{.bash}
-pip install 'ibis-framework[pandas]'
-```
-
-And connect:
-
-```{.python}
-import ibis
-
-con = ibis.pandas.connect()  # <1>
-```
-
-1. Adjust connection parameters as needed.
-
-## `conda`
-
-Install for pandas:
-
-```{.bash}
-conda install -c conda-forge ibis-pandas
-```
-
-And connect:
-
-```{.python}
-import ibis
-
-con = ibis.pandas.connect()  # <1>
-```
-
-1. Adjust connection parameters as needed.
-
-## `mamba`
-
-Install for pandas:
-
-```{.bash}
-mamba install -c conda-forge ibis-pandas
-```
-
-And connect:
-
-```{.python}
-import ibis
-
-con = ibis.pandas.connect()  # <1>
-```
-
-1. Adjust connection parameters as needed.
+::: {.callout-note}
+## The pandas backend was removed in Ibis version 10.0
 
+See [our blog post](../posts/farewell-pandas/index.qmd) on the topic for more information.
 :::
-
-
-
-## 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**.
-
-### Elementwise 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:
-
-```python
-import ibis.expr.datatypes as dt
-from ibis.backends.pandas.udf 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:
-
-```python
-import ibis.expr.datatypes as dt
-from ibis.backends.pandas.udf 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:
-
-```python
-import ibis.expr.datatypes as dt
-from ibis.backends.pandas.udf 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 provide support
-  for applying your UDF to any combination of scalar values and columns.
-- Reductions 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.
-
-::: {.callout-warning}
-## Keyword arguments must be given a default
-
-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:
-
-```python
-import ibis
-import pandas as pd
-df = pd.DataFrame({'a': [1, 2, 3]})
-con = ibis.pandas.connect({'df': df})
-t = con.table('df')
-expr = add_one(t.a)
-expr
-```
-
-And this will receive the `int` 1:
-
-```python
-expr = add_one(1)
-expr
-```
-
-Since the pandas backend passes around `**kwargs` you can accept `**kwargs`
-in your function:
-
-```python
-import ibis.expr.datatypes as dt
-from ibis.backends.pandas.udf 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:
-
-```python
-import ibis.expr.datatypes as dt
-from ibis.backends.pandas.udf 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
-```
-
-```{python}
-#| echo: false
-BACKEND = "Pandas"
-```
-
-{{< include ./_templates/api.qmd >}}

docs/backends_sankey.py

@@ -42,7 +42,7 @@ def to_greyish(hex_code, grey_value=128):
         "SQLite",
         "Trino",
     ],
-    list(category_colors.keys())[2]: ["Dask", "pandas", "Polars"],
+    list(category_colors.keys())[2]: ["Polars"],
 }
 
 nodes, links = [], []

ibis/backends/conftest.py

@@ -4,15 +4,13 @@
 import importlib
 import importlib.metadata
 import itertools
-import operator
 from functools import cache
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 import _pytest
 import pytest
 from packaging.requirements import Requirement
-from packaging.version import parse as vparse
 
 import ibis
 from ibis import util
@@ -30,22 +28,6 @@
     from ibis.backends.tests.base import BackendTest
 
 
-def compare_versions(module_name, given_version, op):
-    try:
-        current_version = importlib.metadata.version(module_name)
-        return op(vparse(current_version), vparse(given_version))
-    except importlib.metadata.PackageNotFoundError:
-        return False
-
-
-def is_newer_than(module_name, given_version):
-    return compare_versions(module_name, given_version, operator.gt)
-
-
-def is_older_than(module_name, given_version):
-    return compare_versions(module_name, given_version, operator.lt)
-
-
 TEST_TABLES = {
     "functional_alltypes": ibis.schema(
         {
@@ -486,7 +468,7 @@ def _setup_backend(request, data_dir, tmp_path_factory, worker_id):
 
 
 @pytest.fixture(
-    params=_get_backends_to_test(discard=("pandas",)),
+    params=_get_backends_to_test(),
     scope="session",
 )
 def ddl_backend(request, data_dir, tmp_path_factory, worker_id):
@@ -501,7 +483,7 @@ def ddl_con(ddl_backend):
 
 
 @pytest.fixture(
-    params=_get_backends_to_test(keep=("pandas", "pyspark")),
+    params=_get_backends_to_test(keep=("pyspark",)),
     scope="session",
 )
 def udf_backend(request, data_dir, tmp_path_factory, worker_id):