feat(pkg-py): Add a new .data module

pkg-py/CHANGELOG.md

@@ -19,6 +19,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 * The current SQL query and title can now be programmatically set through the `.sql()` and `.title()` methods of `QueryChat()`. (#98, #101)
 
+* New `querychat.data` module provides sample datasets (`titanic()` and `tips()`) to make it easier to get started without external dependencies. (#118)
+
 * Added a `.generate_greeting()` method to help you create a greeting message for your querychat bot. (#87)
 
 * Added `querychat_reset_dashboard()` tool for easily resetting the dashboard filters when asked by the user. (#81)

pkg-py/docs/CHANGELOG.md

@@ -19,6 +19,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 * The current SQL query and title can now be programmatically set through the `.sql()` and `.title()` methods of `QueryChat()`. (#98, #101)
 
+* New `querychat.data` module provides sample datasets (`titanic()` and `tips()`) to make it easier to get started without external dependencies. (#118)
+
 * Added a `.generate_greeting()` method to help you create a greeting message for your querychat bot. (#87)
 
 * Added `querychat_reset_dashboard()` tool for easily resetting the dashboard filters when asked by the user. (#81)

pkg-py/docs/build.qmd

@@ -181,14 +181,13 @@ Thanks to Shiny's support for [Jupyter Widgets](https://shiny.posit.co/py/docs/j
 ```python
 import plotly.express as px
 
-from seaborn import load_dataset
 from shiny.express import render, ui
 from shinywidgets import render_plotly
 
 from querychat.express import QueryChat
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 qc.sidebar()
 
 with ui.layout_columns():
@@ -223,12 +222,11 @@ A more useful, but slightly more involved example like the one below might incor
 from shiny.express import render, ui
 from shinywidgets import render_plotly
 from querychat.express import QueryChat
-from seaborn import load_dataset
+from querychat.data import titanic
 from faicons import icon_svg
 import plotly.express as px
 
-titanic = load_dataset("titanic")
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 qc.sidebar()
 
 with ui.layout_column_wrap(fill=False):
@@ -356,11 +354,11 @@ You can use multiple QueryChat instances in a single app to explore different da
 from seaborn import load_dataset
 from shiny.express import render, ui
 from querychat.express import QueryChat
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
 penguins = load_dataset("penguins")
 
-qc_titanic = QueryChat(titanic, "titanic")
+qc_titanic = QueryChat(titanic(), "titanic")
 qc_penguins = QueryChat(penguins, "penguins")
 
 with ui.sidebar():
@@ -396,15 +394,12 @@ Here's a complete example bringing together multiple concepts - a Titanic surviv
 ```python
 from shiny.express import render, ui
 from querychat.express import QueryChat
-from seaborn import load_dataset
+from querychat.data import titanic
 import plotly.express as px
 
-# Load data
-titanic = load_dataset("titanic")
-
 # Create QueryChat
 qc = QueryChat(
-    titanic,
+    titanic(),
     "titanic",
     data_description="Titanic passenger data with survival outcomes",
 )

pkg-py/docs/context.qmd

@@ -12,11 +12,9 @@ For full visibility into the full system prompt that Querychat generates for the
 
 ```python
 from querychat import QueryChat
-from seaborn import load_dataset
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
-
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 print(qc.system_prompt)
 ```
 

pkg-py/docs/data-sources.qmd

@@ -160,12 +160,10 @@ Or, if you have a pandas DataFrame, you can create the DuckDB database like so:
 ```{.python filename="create-duckdb-from-pandas.py"}
 import duckdb
 import pandas as pd
-
-from seaborn import load_dataset
-titanic = load_dataset("titanic")
+from querychat.data import titanic
 
 conn = duckdb.connect("my_database.duckdb")
-conn.register('titanic_df', titanic)
+conn.register('titanic_df', titanic())
 conn.execute("""
     CREATE TABLE titanic AS
     SELECT * FROM titanic_df

pkg-py/docs/index.qmd

@@ -40,11 +40,10 @@ The quickest way to start chatting is to call the `.app()` method, which returns
 
 
 ```{.python filename="titanic-app.py"}
-from seaborn import load_dataset
 from querychat import QueryChat
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
-qc = QueryChat(titanic, "titanic", client="openai/gpt-4.1")
+qc = QueryChat(titanic(), "titanic", client="openai/gpt-4.1")
 app = qc.app()
 ```
 

pkg-py/docs/models.qmd

@@ -10,11 +10,10 @@ To use a particular model, pass a `"{provider}/{model}"` string to the `client`
 
 ```python
 from querychat import QueryChat
-from seaborn import load_dataset
-titanic = load_dataset("titanic")
+from querychat.data import titanic
 
 qc = QueryChat(
-    titanic,
+    titanic(),
     "titanic",
     client="anthropic/claude-sonnet-4-5"
 )

pkg-py/docs/tools.qmd

@@ -23,10 +23,9 @@ Here's a basic example of this tool in action with the `.app()` method. Notice h
 
 ```{.python filename="titanic-app.py"}
 from querychat import QueryChat
-from seaborn import load_dataset
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 app = qc.app()
 ```
 
@@ -46,10 +45,9 @@ Here's an example of it in action:
 
 ```{.python filename="titanic-app.py"}
 from querychat import QueryChat
-from seaborn import load_dataset
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 app = qc.app()
 ```
 

pkg-py/examples/01-hello-app.py

@@ -1,6 +1,5 @@
-from seaborn import load_dataset
 from querychat import QueryChat
+from querychat.data import titanic
 
-titanic = load_dataset("titanic")
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 app = qc.app()

pkg-py/examples/02-prompt-app.py

@@ -1,15 +1,13 @@
 
 from pathlib import Path
-from seaborn import load_dataset
 from querychat import QueryChat
-
-titanic = load_dataset("titanic")
+from querychat.data import titanic
 
 greeting = Path(__file__).parent / "greeting.md"
 data_desc = Path(__file__).parent / "data_description.md"
 
 qc = QueryChat(
-    titanic,
+    titanic(),
     "titanic",
     greeting=greeting,
     data_description=data_desc,

pkg-py/examples/03-sidebar-core-app.py

@@ -1,11 +1,9 @@
-from seaborn import load_dataset
 from shiny import App, render, ui
 from querychat import QueryChat
-
-titanic = load_dataset("titanic")
+from querychat.data import titanic
 
 # 1. Provide data source to QueryChat
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 
 app_ui = ui.page_sidebar(
     # 2. Create sidebar chat control

pkg-py/examples/03-sidebar-express-app.py

@@ -1,11 +1,9 @@
-from seaborn import load_dataset
 from shiny.express import render, ui
 from querychat.express import QueryChat
-
-titanic = load_dataset("titanic")
+from querychat.data import titanic
 
 # 1. Provide data source to QueryChat
-qc = QueryChat(titanic, "titanic")
+qc = QueryChat(titanic(), "titanic")
 
 # 2. Add sidebar chat control
 qc.sidebar()

pkg-py/src/querychat/data/__init__.py

@@ -0,0 +1,68 @@
+"""
+Sample datasets for getting started with querychat.
+
+This module provides easy access to sample datasets that can be used with QueryChat
+to quickly get started without needing to install additional dependencies.
+"""
+
+from __future__ import annotations
+
+from importlib.resources import files
+
+import pandas as pd
+
+
+def titanic() -> pd.DataFrame:
+    """
+    Load the Titanic dataset.
+
+    This dataset contains information about passengers on the Titanic, including
+    whether they survived, their class, age, sex, and other demographic information.
+
+    Returns
+    -------
+    pandas.DataFrame
+        A DataFrame with 891 rows and 15 columns containing Titanic passenger data.
+
+    Examples
+    --------
+    >>> from querychat.data import titanic
+    >>> from querychat import QueryChat
+    >>> df = titanic()
+    >>> qc = QueryChat(df, "titanic")
+    >>> app = qc.app()
+
+    """
+    # Get the path to the gzipped CSV file using importlib.resources
+    data_file = files("querychat.data") / "titanic.csv.gz"
+    return pd.read_csv(str(data_file), compression="gzip")
+
+
+def tips() -> pd.DataFrame:
+    """
+    Load the tips dataset.
+
+    This dataset contains information about restaurant tips, including the total
+    bill, tip amount, and information about the party (sex, smoker status, day,
+    time, and party size).
+
+    Returns
+    -------
+    pandas.DataFrame
+        A DataFrame with 244 rows and 7 columns containing restaurant tip data.
+
+    Examples
+    --------
+    >>> from querychat.data import tips
+    >>> from querychat import QueryChat
+    >>> df = tips()
+    >>> qc = QueryChat(df, "tips")
+    >>> app = qc.app()
+
+    """
+    # Get the path to the gzipped CSV file using importlib.resources
+    data_file = files("querychat.data") / "tips.csv.gz"
+    return pd.read_csv(str(data_file), compression="gzip")
+
+
+__all__ = ["tips", "titanic"]