Use Narwhals to support Polars and Modin DataFrames

.github/workflows/continuous-integration.yml

@@ -37,23 +37,35 @@ jobs:
         uses: github/codeql-action/analyze@v3
 
   pytest:
+    name: >
+      ${{ matrix.python-version }}
+      ${{ matrix.pandas-version && format('(pandas {0})', matrix.pandas-version)}}
+      ${{ matrix.numpy-version && format('(numpy {0})', matrix.numpy-version)}}
+      ${{ matrix.modifiers}}
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", "3.11", "3.12", "3.13"]
-        pandas-version: [latest]
-        numpy-version: [latest]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12", "3.13"]
+        pandas-version: ['']
+        numpy-version: ['']
+        modifiers: ['']
         include:
-          - python-version: 3.7
-            pandas-version: '<1.0'
+          - python-version: 3.9
           - python-version: 3.9
             pandas-version: '<2.0'
             numpy-version: '<2.0'
           - python-version: "3.13"
             pandas-version: pre
-            polars: true
           - python-version: "3.13"
-            uninstall_jinja2: true
+            modifiers: "polars"
+          - python-version: "3.13"
+            modifiers: "ibis"
+          - python-version: "3.12"
+            modifiers: "modin"
+          - python-version: "3.13"
+            modifiers: "uninstall_narwhals"
+          - python-version: "3.13"
+            modifiers: "uninstall_jinja2"
     runs-on: ubuntu-20.04
     steps:
       - name: Checkout
@@ -70,31 +82,42 @@ jobs:
       - name: Install a development version of 'itables'
         run: pip install -e .[test]
 
-      - name: Install pandas latest
-        if: matrix.pandas-version == 'latest'
+      - name: Install latest pandas
+        if: ${{!matrix.pandas-version}}
         run: pip install pandas
       - name: Install pandas pre-release
         if: matrix.pandas-version == 'pre'
         run: pip install pandas --pre
       - name: Install pandas ${{ matrix.pandas-version }}
-        if: matrix.pandas-version != 'pre' && matrix.pandas-version != 'latest'
+        if: matrix.pandas-version && matrix.pandas-version != 'pre'
         run: pip install 'pandas${{ matrix.pandas-version }}'
       - name: Install numpy ${{ matrix.numpy-version }}
-        if: matrix.numpy-version != 'latest'
+        if: matrix.numpy-version
         run: pip install 'numpy${{ matrix.numpy-version }}'
 
       - name: Install polars
-        if: matrix.polars
-        run: pip install -e .[polars]
+        if: matrix.modifiers == 'polars'
+        run: pip install polars
 
-      - name: Install shiny
-        if: matrix.python-version != '3.7'
-        run: pip install "shiny>=1.0"
+      - name: Install ibis
+        if: matrix.modifiers == 'ibis'
+        run: pip install 'ibis-framework[duckdb]'
+
+      - name: Install modin
+        if: matrix.modifiers == 'modin'
+        run: pip install modin[all]
+
+      - name: Uninstall narwhals
+        if: matrix.modifiers == 'uninstall_narwhals'
+        run: pip uninstall narwhals -y
 
       - name: Uninstall jinja2
-        if: matrix.uninstall_jinja2
+        if: matrix.modifiers == 'uninstall_jinja2'
         run: pip uninstall jinja2 -y
 
+      - name: Install shiny
+        run: pip install "shiny>=1.0"
+
       - name: Install a Jupyter Kernel
         run: python -m ipykernel install --name itables --user
 

docs/_toc.yml

@@ -32,3 +32,4 @@ parts:
     chapters:
     - file: sample_dataframes
     - file: polars_dataframes
+    - file: modin_dataframes

docs/changelog.md

@@ -1,6 +1,13 @@
 ITables ChangeLog
 =================
 
+2.3.0-dev
+---------
+
+**Added**
+- In addition to Pandas and Polars, ITables now support Modin DataFrames ([#325](https://github.com/mwouts/itables/issues/325)). Under the hoods we use [Narwhals](https://github.com/narwhals-dev/narwhals) to handle the different types of DataFrames. Thanks to [Dea María Léon](https://github.com/DeaMariaLeon) and to [Marco Gorelli](https://github.com/MarcoGorelli) for making this work, and for developing Narwhals too!
+
+
 2.2.4 (2024-12-07)
 ------------------
 

docs/ibis_dataframes.md

@@ -0,0 +1,183 @@
+---
+jupytext:
+  formats: md:myst
+  notebook_metadata_filter: -jupytext.text_representation.jupytext_version
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: itables
+  language: python
+  name: itables
+---
+
+# Sample dataframes
+
+In this notebook we make sure that our test [Ibis](https://ibis-project.org/) dataframes are displayed nicely with the default `itables` settings.
+
+```{code-cell}
+from itables import init_notebook_mode, show
+from itables.sample_dfs import get_dict_of_test_ibis_dfs
+
+dict_of_test_dfs = get_dict_of_test_ibis_dfs()
+init_notebook_mode(all_interactive=True)
+```
+
+## empty
+
+```{code-cell}
+show(dict_of_test_dfs["empty"])
+```
+
+## No rows
+
+```{code-cell}
+show(dict_of_test_dfs["no_rows"])
+```
+
+## No rows one column
+
+```{code-cell}
+show(dict_of_test_dfs["no_rows_one_column"])
+```
+
+## No columns
+
+```{code-cell}
+show(dict_of_test_dfs["no_columns"])
+```
+
+## No columns one row
+
+```{code-cell}
+show(dict_of_test_dfs["no_columns_one_row"])
+```
+
+## bool
+
+```{code-cell}
+show(dict_of_test_dfs["bool"])
+```
+
+## Nullable boolean
+
+```{code-cell}
+show(dict_of_test_dfs["nullable_boolean"])
+```
+
+## int
+
+```{code-cell}
+show(dict_of_test_dfs["int"])
+```
+
+## Nullable integer
+
+```{code-cell}
+show(dict_of_test_dfs["nullable_int"])
+```
+
+## float
+
+```{code-cell}
+show(dict_of_test_dfs["float"])
+```
+
+## str
+
+```{code-cell}
+show(dict_of_test_dfs["str"])
+```
+
+## time
+
+```{code-cell}
+show(dict_of_test_dfs["time"])
+```
+
+## object
+
+```{code-cell}
+show(dict_of_test_dfs["object"])
+```
+
+## ordered_categories
+
+```{code-cell}
+show(dict_of_test_dfs["ordered_categories"])
+```
+
+## ordered_categories_in_multiindex
+
+```{code-cell}
+show(dict_of_test_dfs["ordered_categories_in_multiindex"])
+```
+
+## multiindex
+
+```{code-cell}
+show(dict_of_test_dfs["multiindex"])
+```
+
+## countries
+
+```{code-cell}
+:tags: [full-width]
+
+show(dict_of_test_dfs["countries"])
+```
+
+## capital
+
+```{code-cell}
+show(dict_of_test_dfs["capital"])
+```
+
+## complex_index
+
+```{code-cell}
+:tags: [full-width]
+
+show(dict_of_test_dfs["complex_index"])
+```
+
+## int_float_str
+
+```{code-cell}
+show(dict_of_test_dfs["int_float_str"])
+```
+
+## wide
+
+```{code-cell}
+:tags: [full-width]
+
+show(dict_of_test_dfs["wide"], maxBytes=100000, maxColumns=100, scrollX=True)
+```
+
+## long_column_names
+
+```{code-cell}
+:tags: [full-width]
+
+show(dict_of_test_dfs["long_column_names"], scrollX=True)
+```
+
+## duplicated_columns
+
+```{code-cell}
+show(dict_of_test_dfs["duplicated_columns"])
+```
+
+## named_column_index
+
+```{code-cell}
+show(dict_of_test_dfs["named_column_index"])
+```
+
+## big_integers
+
+```{code-cell}
+show(dict_of_test_dfs["big_integers"])
+```