update system prompt & claude.md (#6437)

## 📝 Summary

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If this PR fixes any issues, list them here by number (e.g., Fixes
#123).
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- Use polars instead of pandas
- Altair instead of pyplot
- Add a short sql cell guide (don't return df, pass in engine name)

## 🔍 Description of Changes

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problem addressed and how it was resolved. If applicable, provide before
and after comparisons, screenshots, or any relevant details to help
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## 📋 Checklist

- [x] I have read the [contributor
guidelines](https://github.com/marimo-team/marimo/blob/main/CONTRIBUTING.md).
- [ ] For large changes, or changes that affect the public API: this
change was discussed or approved through an issue, on
[Discord](https://marimo.io/discord?ref=pr), or the community
[discussions](https://github.com/marimo-team/marimo/discussions) (Please
provide a link if applicable).
- [x] I have added tests for the changes made.
- [x] I have run the code and verified that it works as expected.

Author: Light2Dark

docs/_static/CLAUDE.md

@@ -46,7 +46,7 @@ Marimo's reactivity means:
 
 <data_handling>
 
-- Use pandas for data manipulation
+- Use polars for data manipulation
 - Implement proper data validation
 - Handle missing values appropriately
 - Use efficient data structures
@@ -56,7 +56,7 @@ Marimo's reactivity means:
 <visualization>
 - For matplotlib: use plt.gca() as the last expression instead of plt.show()
 - For plotly: return the figure object directly
-- For altair: return the chart object directly
+- For altair: return the chart object directly. Add tooltips where appropriate. You can pass polars dataframes directly to altair.
 - Include proper labels, titles, and color schemes
 - Make visualizations interactive where appropriate
 </visualization>
@@ -79,7 +79,7 @@ Marimo's reactivity means:
 </data_sources>
 
 <sql>
-- When writing duckdb, prefer using marimo's SQL cells, which start with _df = mo.sql(query)
+- When writing duckdb, prefer using marimo's SQL cells, which start with df = mo.sql(f"""<your query>""") for DuckDB, or df = mo.sql(f"""<your query>""", engine=engine) for other SQL engines.
 - See the SQL with duckdb example for an example on how to do this
 - Don't add comments in cells that use mo.sql()
 - Consider using \`vega_datasets\` for common example datasets
@@ -132,7 +132,8 @@ Common issues and solutions:
 <example title="Basic UI with reactivity">
 # Cell 1
 import marimo as mo
-import matplotlib.pyplot as plt
+import altair as alt
+import polars as pl
 import numpy as np
 
 # Cell 2
@@ -151,59 +152,64 @@ n_points  # Display the slider
 x = np.random.rand(n_points.value)
 y = np.random.rand(n_points.value)
 
-plt.figure(figsize=(8, 6))
-plt.scatter(x, y, alpha=0.7)
-plt.title(f"Scatter plot with {n_points.value} points")
-plt.xlabel("X axis")
-plt.ylabel("Y axis")
-plt.gca()  # Return the current axes to display the plot
+df = pl.DataFrame({"x": x, "y": y})
+
+chart = alt.Chart(df).mark_circle(opacity=0.7).encode(
+    x=alt.X('x', title='X axis'),
+    y=alt.Y('y', title='Y axis')
+).properties(
+    title=f"Scatter plot with {n_points.value} points",
+    width=400,
+    height=300
+)
+
+chart
 </example>
 
 <example title="Data explorer">
 # Cell 1
 import marimo as mo
-import pandas as pd
+import polars as pl
 from vega_datasets import data
 
 # Cell 2
 
 # Load and display dataset with interactive explorer
 
-cars_df = data.cars()
+cars_df = pl.DataFrame(data.cars())
 mo.ui.data_explorer(cars_df)
 </example>
 
 <example title="Multiple UI elements">
 # Cell 1
 import marimo as mo
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
+import polars as pl
+import altair as alt
 
 # Cell 2
 
 # Load dataset
 
-iris = sns.load_dataset('iris')
+iris = pl.read_csv("hf://datasets/scikit-learn/iris/Iris.csv")
 
 # Cell 3
 
 # Create UI elements
 
 species_selector = mo.ui.dropdown(
-    options=["All"] + iris["species"].unique().tolist(),
+    options=["All"] + iris["Species"].unique().to_list(),
     value="All",
-    label="Species"
+    label="Species",
 )
 x_feature = mo.ui.dropdown(
-    options=iris.select_dtypes('number').columns.tolist(),
-    value="sepal_length",
-    label="X Feature"
+    options=iris.select(pl.col(pl.Float64, pl.Int64)).columns,
+    value="SepalLengthCm",
+    label="X Feature",
 )
 y_feature = mo.ui.dropdown(
-    options=iris.select_dtypes('number').columns.tolist(),
-    value="sepal_width",
-    label="Y Feature"
+    options=iris.select(pl.col(pl.Float64, pl.Int64)).columns,
+    value="SepalWidthCm",
+    label="Y Feature",
 )
 
 # Display UI elements in a horizontal stack
@@ -214,36 +220,45 @@ mo.hstack([species_selector, x_feature, y_feature])
 
 # Filter data based on selection
 
-filtered_data = iris if species_selector.value == "All" else iris[iris["species"] == species_selector.value]
+filtered_data = iris if species_selector.value == "All" else iris.filter(pl.col("Species") == species_selector.value)
 
 # Create visualization based on UI selections
 
-plt.figure(figsize=(10, 6))
-sns.scatterplot(
-    data=filtered_data,
-    x=x_feature.value,
-    y=y_feature.value,
-    hue="species"
+chart = alt.Chart(filtered_data).mark_circle().encode(
+    x=alt.X(x_feature.value, title=x_feature.value),
+    y=alt.Y(y_feature.value, title=y_feature.value),
+    color='Species'
+).properties(
+    title=f"{y_feature.value} vs {x_feature.value}",
+    width=500,
+    height=400
 )
-plt.title(f"{y_feature.value} vs {x_feature.value}")
-plt.gca()
+
+chart
 </example>
 
 <example title="Interactive chart with Altair">
 # Cell 1
 import marimo as mo
 import altair as alt
-import pandas as pd
+import polars as pl
 
 # Cell 2
 
 # Load dataset
 
-cars_df = pd.read_csv('<https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json>')
-_chart = alt.Chart(cars_df).mark_point().encode(
-    x='Horsepower',
-    y='Miles_per_Gallon',
-    color='Origin',
+weather = pl.read_csv("https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/weather.csv")
+weather_dates = weather.with_columns(
+    pl.col("date").str.strptime(pl.Date, format="%Y-%m-%d")
+)
+_chart = (
+    alt.Chart(weather_dates)
+    .mark_point()
+    .encode(
+        x="date:T",
+        y="temp_max",
+        color="location",
+    )
 )
 
 chart = mo.ui.altair_chart(_chart)
@@ -279,16 +294,21 @@ else:
 <example title="SQL with duckdb">
 # Cell 1
 import marimo as mo
+import polars as pl
 
 # Cell 2
 
 # Load dataset
 
-cars_df = pd.read_csv('<https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json>')
+weather = pl.read_csv("https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/weather.csv")
 
 # Cell 3
 
-_df = mo.sql("SELECT * from cars_df WHERE Miles_per_Gallon > 20")
+seattle_weather_df = mo.sql(
+    f"""
+    SELECT * FROM weather WHERE location = 'Seattle';
+    """
+)
 </example>
 
 <example title="Writing LaTeX in markdown">
@@ -297,10 +317,11 @@ import marimo as mo
 
 # Cell 2
 
-mo.md(r"""
-
+mo.md(
+    r"""
 The quadratic function $f$ is defined as
 
 $$f(x) = x^2.$$
-""")
+"""
+)
 </example>