feat(python!): Use Altair in DataFrame.plot

.github/workflows/benchmark.yml

@@ -48,7 +48,12 @@ jobs:
 
       - name: Install Python dependencies
         working-directory: py-polars
-        run: uv pip install --compile-bytecode -r requirements-dev.txt -r requirements-ci.txt --verbose
+        run: |
+          # Install typing-extensions separately whilst the `--extra-index-url` in `requirements-ci.txt`
+          # doesn't have an up-to-date typing-extensions, see
+          # https://github.com/astral-sh/uv/issues/6028#issuecomment-2287232150
+          uv pip install -U typing-extensions 
+          uv pip install --compile-bytecode -r requirements-dev.txt -r requirements-ci.txt --verbose
 
       - name: Set up Rust
         run: rustup show

.github/workflows/test-coverage.yml

@@ -103,7 +103,12 @@ jobs:
 
       - name: Install Python dependencies
         working-directory: py-polars
-        run: uv pip install --compile-bytecode -r requirements-dev.txt -r requirements-ci.txt --verbose
+        run: |
+          # Install typing-extensions separately whilst the `--extra-index-url` in `requirements-ci.txt`
+          # doesn't have an up-to-date typing-extensions, see
+          # https://github.com/astral-sh/uv/issues/6028#issuecomment-2287232150
+          uv pip install -U typing-extensions 
+          uv pip install --compile-bytecode -r requirements-dev.txt -r requirements-ci.txt --verbose
 
       - name: Set up Rust
         run: rustup component add llvm-tools-preview

.github/workflows/test-python.yml

@@ -66,6 +66,10 @@ jobs:
       - name: Install Python dependencies
         run: |
           pip install uv
+          # Install typing-extensions separately whilst the `--extra-index-url` in `requirements-ci.txt`
+          # doesn't have an up-to-date typing-extensions, see
+          # https://github.com/astral-sh/uv/issues/6028#issuecomment-2287232150
+          uv pip install -U typing-extensions
           uv pip install --compile-bytecode -r requirements-dev.txt -r requirements-ci.txt --verbose
 
       - name: Set up Rust

docs/requirements.txt

@@ -1,10 +1,11 @@
+altair
 pandas
 pyarrow
 graphviz
+hvplot
 matplotlib
 seaborn
 plotly
-altair
 numba
 numpy
 

docs/src/python/user-guide/misc/visualization.py

@@ -3,30 +3,33 @@
 
 path = "docs/data/iris.csv"
 
-df = pl.scan_csv(path).group_by("species").agg(pl.col("petal_length").mean()).collect()
+df = pl.read_csv(path)
 print(df)
 # --8<-- [end:dataframe]
 
 """
 # --8<-- [start:hvplot_show_plot]
-df.plot.bar(
-    x="species",
-    y="petal_length",
+import hvplot.polars
+df.hvplot.scatter(
+    x="sepal_width",
+    y="sepal_length",
+    by="species",
     width=650,
 )
 # --8<-- [end:hvplot_show_plot]
 """
 
 # --8<-- [start:hvplot_make_plot]
-import hvplot
+import hvplot.polars
 
-plot = df.plot.bar(
-    x="species",
-    y="petal_length",
+plot = df.hvplot.scatter(
+    x="sepal_width",
+    y="sepal_length",
+    by="species",
     width=650,
 )
-hvplot.save(plot, "docs/images/hvplot_bar.html")
-with open("docs/images/hvplot_bar.html", "r") as f:
+hvplot.save(plot, "docs/images/hvplot_scatter.html")
+with open("docs/images/hvplot_scatter.html", "r") as f:
     chart_html = f.read()
     print(f"{chart_html}")
 # --8<-- [end:hvplot_make_plot]
@@ -35,7 +38,12 @@
 # --8<-- [start:matplotlib_show_plot]
 import matplotlib.pyplot as plt
 
-plt.bar(x=df["species"], height=df["petal_length"])
+fig, ax = plt.subplots()
+ax.scatter(
+    x=df["sepal_width"],
+    y=df["sepal_length"],
+    c=df["species"].cast(pl.Categorical).to_physical(),
+)
 # --8<-- [end:matplotlib_show_plot]
 """
 
@@ -44,34 +52,43 @@
 
 import matplotlib.pyplot as plt
 
-plt.bar(x=df["species"], height=df["petal_length"])
-plt.savefig("docs/images/matplotlib_bar.png")
-with open("docs/images/matplotlib_bar.png", "rb") as f:
+fig, ax = plt.subplots()
+ax.scatter(
+    x=df["sepal_width"],
+    y=df["sepal_length"],
+    c=df["species"].cast(pl.Categorical).to_physical(),
+)
+fig.savefig("docs/images/matplotlib_scatter.png")
+with open("docs/images/matplotlib_scatter.png", "rb") as f:
     png = base64.b64encode(f.read()).decode()
     print(f'<img src="data:image/png;base64, {png}"/>')
 # --8<-- [end:matplotlib_make_plot]
 
 """
 # --8<-- [start:seaborn_show_plot]
 import seaborn as sns
-sns.barplot(
+sns.scatterplot(
     df,
-    x="species",
-    y="petal_length",
+    x="sepal_width",
+    y="sepal_length",
+    hue="species",
 )
 # --8<-- [end:seaborn_show_plot]
 """
 
 # --8<-- [start:seaborn_make_plot]
 import seaborn as sns
+import matplotlib.pyplot as plt
 
-sns.barplot(
+fig, ax = plt.subplots()
+ax = sns.scatterplot(
     df,
-    x="species",
-    y="petal_length",
+    x="sepal_width",
+    y="sepal_length",
+    hue="species",
 )
-plt.savefig("docs/images/seaborn_bar.png")
-with open("docs/images/seaborn_bar.png", "rb") as f:
+fig.savefig("docs/images/seaborn_scatter.png")
+with open("docs/images/seaborn_scatter.png", "rb") as f:
     png = base64.b64encode(f.read()).decode()
     print(f'<img src="data:image/png;base64, {png}"/>')
 # --8<-- [end:seaborn_make_plot]
@@ -80,51 +97,60 @@
 # --8<-- [start:plotly_show_plot]
 import plotly.express as px
 
-px.bar(
+px.scatter(
     df,
-    x="species",
-    y="petal_length",
-    width=400,
+    x="sepal_width",
+    y="sepal_length",
+    color="species",
+    width=650,
 )
 # --8<-- [end:plotly_show_plot]
 """
 
 # --8<-- [start:plotly_make_plot]
 import plotly.express as px
 
-fig = px.bar(
+fig = px.scatter(
     df,
-    x="species",
-    y="petal_length",
+    x="sepal_width",
+    y="sepal_length",
+    color="species",
     width=650,
 )
-fig.write_html("docs/images/plotly_bar.html", full_html=False, include_plotlyjs="cdn")
-with open("docs/images/plotly_bar.html", "r") as f:
+fig.write_html(
+    "docs/images/plotly_scatter.html", full_html=False, include_plotlyjs="cdn"
+)
+with open("docs/images/plotly_scatter.html", "r") as f:
     chart_html = f.read()
     print(f"{chart_html}")
 # --8<-- [end:plotly_make_plot]
 
 """
 # --8<-- [start:altair_show_plot]
-import altair as alt
-
-alt.Chart(df, width=700).mark_bar().encode(x="species:N", y="petal_length:Q")
+(
+    df.plot.point(
+        x="sepal_length",
+        y="sepal_width",
+        color="species",
+    )
+    .properties(width=500)
+    .configure_scale(zero=False)
+)
 # --8<-- [end:altair_show_plot]
 """
 
 # --8<-- [start:altair_make_plot]
-import altair as alt
-
 chart = (
-    alt.Chart(df, width=600)
-    .mark_bar()
-    .encode(
-        x="species:N",
-        y="petal_length:Q",
+    df.plot.point(
+        x="sepal_length",
+        y="sepal_width",
+        color="species",
     )
+    .properties(width=500)
+    .configure_scale(zero=False)
 )
-chart.save("docs/images/altair_bar.html")
-with open("docs/images/altair_bar.html", "r") as f:
+chart.save("docs/images/altair_scatter.html")
+with open("docs/images/altair_scatter.html", "r") as f:
     chart_html = f.read()
     print(f"{chart_html}")
 # --8<-- [end:altair_make_plot]

docs/user-guide/misc/visualization.md

@@ -2,17 +2,48 @@
 
 Data in a Polars `DataFrame` can be visualized using common visualization libraries.
 
-We illustrate plotting capabilities using the Iris dataset. We scan a CSV and then do a group-by on the `species` column and get the mean of the `petal_length`.
+We illustrate plotting capabilities using the Iris dataset. We read a CSV and then
+plot one column against another, colored by a yet another column.
 
 {{code_block('user-guide/misc/visualization','dataframe',[])}}
 
 ```python exec="on" result="text" session="user-guide/misc/visualization"
 --8<-- "python/user-guide/misc/visualization.py:dataframe"
 ```
 
-## Built-in plotting with hvPlot
+## Built-in plotting with Altair
 
-Polars has a `plot` method to create interactive plots using [hvPlot](https://hvplot.holoviz.org/).
+Polars has a `plot` method to create plots using [Altair](https://altair-viz.github.io/):
+
+{{code_block('user-guide/misc/visualization','altair_show_plot',[])}}
+
+```python exec="on" session="user-guide/misc/visualization"
+--8<-- "python/user-guide/misc/visualization.py:altair_make_plot"
+```
+
+This is shorthand for:
+
+```python
+import altair as alt
+
+(
+    alt.Chart(df).mark_point().encode(
+        x="sepal_length",
+        y="sepal_width",
+        color="species",
+    )
+    .properties(width=500)
+    .configure_scale(zero=False)
+)
+```
+
+and is only provided for convenience, and to signal that Altair is known to work well with
+Polars.
+
+## hvPlot
+
+If you import `hvplot.polars`, then it registers a `hvplot`
+method which you can use to create interactive plots using [hvPlot](https://hvplot.holoviz.org/).
 
 {{code_block('user-guide/misc/visualization','hvplot_show_plot',[])}}
 
@@ -22,18 +53,23 @@ Polars has a `plot` method to create interactive plots using [hvPlot](https://hv
 
 ## Matplotlib
 
-To create a bar chart we can pass columns of a `DataFrame` directly to Matplotlib as a `Series` for each column. Matplotlib does not have explicit support for Polars objects but Matplotlib can accept a Polars `Series` because it can convert each Series to a numpy array, which is zero-copy for numeric
-data without null values.
+To create a scatter plot we can pass columns of a `DataFrame` directly to Matplotlib as a `Series` for each column.
+Matplotlib does not have explicit support for Polars objects but can accept a Polars `Series` by
+converting it to a NumPy array (which is zero-copy for numeric data without null values).
+
+Note that because the column `'species'` isn't numeric, we need to first convert it to numeric values so that
+it can be passed as an argument to `c`.
 
 {{code_block('user-guide/misc/visualization','matplotlib_show_plot',[])}}
 
 ```python exec="on" session="user-guide/misc/visualization"
 --8<-- "python/user-guide/misc/visualization.py:matplotlib_make_plot"
 ```
 
-## Seaborn, Plotly & Altair
+## Seaborn and Plotly
 
-[Seaborn](https://seaborn.pydata.org/), [Plotly](https://plotly.com/) & [Altair](https://altair-viz.github.io/) can accept a Polars `DataFrame` by leveraging the [dataframe interchange protocol](https://data-apis.org/dataframe-api/), which offers zero-copy conversion where possible.
+[Seaborn](https://seaborn.pydata.org/) and [Plotly](https://plotly.com/) can accept a Polars `DataFrame` by leveraging the [dataframe interchange protocol](https://data-apis.org/dataframe-api/), which offers zero-copy conversion where possible. Note
+that the protocol does not support all Polars data types (e.g. `List`) so your mileage may vary here.
 
 ### Seaborn
 
@@ -50,11 +86,3 @@ data without null values.
 ```python exec="on" session="user-guide/misc/visualization"
 --8<-- "python/user-guide/misc/visualization.py:plotly_make_plot"
 ```
-
-### Altair
-
-{{code_block('user-guide/misc/visualization','altair_show_plot',[])}}
-
-```python exec="on" session="user-guide/misc/visualization"
---8<-- "python/user-guide/misc/visualization.py:altair_make_plot"
-```