Merge branch 'voila_app' of https://github.com/ploomber/sql into voil…

…a_app

.gitignore

@@ -198,3 +198,6 @@ products/*
 pipeline/products/datadownload.ipynb
 pipeline/products/eda-pipeline.ipynb
 *.metadata
+
+!docs/intro-to-dashboards-and-apps/data/database/car_data.*
+!pipeline/data/database/car_data.*

README.md

@@ -2,6 +2,20 @@
 
 [![Documentation Status](https://readthedocs.org/projects/ploomber-sql/badge/?version=latest)](https://ploomber-sql.readthedocs.io/en/latest/?badge=latest)
 
+## Important links
+
+Visit the Jupyter book: https://ploomber-sql.readthedocs.io/en/latest/index.html 
+
+You can explore hands on exercises via Google colab on the `colabs` folder in this repository.
+
+To do so:
+
+1. Open [Google Colab](https://colab.research.google.com/)
+2. To open a Jupyter notebook from this repository, change to the GitHub tab, and paste https://github.com/ploomber/sql
+3. Press the search icon 🔍
+4. Select a notebook
+
+![](sql-ploomber-colab.png)
 
 ## Set up for contributors
 

colabs/advanced-querying-techniques/ctes.ipynb

@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install jupysql --upgrade jupysql-plugin --upgrade duckdb-engine --quiet"
+    "%pip install jupysql --quiet"
    ]
   },
   {
@@ -50,18 +50,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e9f8c2a1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/bin/bash: wget: command not found\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# If you are running this from Google Colab\n",
     "!wget https://raw.githubusercontent.com/ploomber/sql/main/banking.py # noqa E402"

docs/advanced-querying-techniques/advanced-aggregations.md

@@ -33,7 +33,7 @@ Let's first run the installations and setup before running any queries.
 This code installs JupySQL, DuckDB, and Pandas in your environment. We will be using these moving forward.
 
 ```{code-cell} ipython3
-%pip install jupysql --upgrade duckdb-engine pandas --quiet
+%pip install jupysql pandas --quiet
 ```
 
 ## Load the data
@@ -410,6 +410,13 @@ GROUP BY GROUPING SETS ((date, duration), (status, duration));
 </details>
 <!-- #endregion -->
 
+Delete table
+
+```{code-cell} ipython3
+%%sql
+DROP TABLE loan;
+```
+
 ## Wrapping Up
 
 In this section, we introduced advanced aggregation functions. To summarize:

docs/advanced-querying-techniques/advanced-joins.md

@@ -23,7 +23,7 @@ kernelspec:
 This code installs JupySQL, DuckDB, and Pandas in your environment. We will be using these moving forward.
 
 ```{code-cell} ipython3
-%pip install jupysql --upgrade duckdb-engine pandas --quiet
+%pip install jupysql pandas --quiet
 ```
 
 ## Load the data
@@ -253,6 +253,16 @@ JOIN s1.loan l ON a.account_id = l.account_id;
 </details>
 <!-- #endregion -->
 
+Delete tables
+
+```{code-cell} ipython3
+%%sql
+DROP TABLE s1.account;
+DROP TABLE s1.loan;
+DROP TABLE s1.district;
+DROP SCHEMA s1;
+```
+
 ## Conclusion
 
 In this tutorial, we have explored advanced join operations in SQL, including nested-loop joins, merge joins, hash joins, internal joins, and the concept of join hints. We learned how to utilize these different join methods in SQL queries and understood the specific scenarios in which each type of join is most efficient.

docs/advanced-querying-techniques/ctes.md

@@ -24,7 +24,7 @@ In this tutorial, you will learn about Common Table Expressions (CTEs) in SQL an
 This code installs JupySQL, and DuckDB in your environment. We will be using these moving forward.
 
 ```{code-cell} ipython3
-%pip install jupysql --upgrade jupysql-plugin --upgrade duckdb-engine --quiet
+%pip install jupysql jupysql-plugin --quiet
 ```
 
 We continue to work with the Bank and Marketing data set.
@@ -404,6 +404,15 @@ The importance of checking the data before and after the operations is to verify
 </details>
 <!-- #endregion -->
 
+Delete tables
+
+```{code-cell} ipython3
+%%sql
+DROP TABLE IF EXISTS s1.district;
+DROP TABLE IF EXISTS bank;
+DROP SCHEMA s1;
+```
+
 ## Summary
 
 In this tutorial we learned:

docs/advanced-querying-techniques/subqueries.md

@@ -28,7 +28,7 @@ Let's demonstrate how powerful subqueries are by continuing with our banking dat
 This code installs JupySQL, DuckDB, and Pandas in your environment. We will be using these moving forward.
 
 ```{code-cell} ipython3
-%pip install jupysql --upgrade duckdb-engine pandas --quiet
+%pip install jupysql pandas --quiet
 ```
 
 ## Load the data
@@ -487,6 +487,15 @@ WHERE amount > (
 </details>
 <!-- #endregion -->
 
+Delete tables
+
+```{code-cell} ipython3
+%%sql
+DROP TABLE IF EXISTS s1.loan;
+DROP TABLE IF EXISTS s1.account;
+DROP TABLE IF EXISTS s1.district;
+DROP SCHEMA s1;
+```
 
 ## Wrapping Up
 

docs/interactive-queries-and-parameterization/make-your-queries-interactive.md

@@ -33,7 +33,7 @@ The installation of `ipywidgets` was covered previously [here](https://ploomber-
 This code installs JupySQL, and DuckDB in your environment. We will be using these moving forward.
 
 ```{code-cell} ipython3
-%pip install jupysql --upgrade jupysql-plugin --upgrade duckdb-engine --quiet
+%pip install jupysql jupysql-plugin --quiet
 ```
 
 We continue to work with the Bank and Marketing data set.
@@ -439,6 +439,13 @@ This ends the Interactive Queries and Parameterization module. We hope you use t
 
 <!-- #endregion -->
 
+Delete table
+
+```{code-cell} ipython3
+%%sql
+DROP TABLE IF EXISTS bank;
+```
+
 ## References
 
 “Simple Widget Introduction#.” Simple Widget Introduction - Jupyter Widgets 8.0.5 documentation, n.d. https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Basics.html.

docs/interactive-queries-and-parameterization/parameterize-sql-queries.md

@@ -25,7 +25,7 @@ In the last module, you learned how to use SQL within Jupyter notebooks with Jup
 This code installs JupySQL, and DuckDB in your environment. We will be using these moving forward.
 
 ```{code-cell} ipython3
-%pip install jupysql --upgrade jupysql-plugin --upgrade duckdb-engine --quiet
+%pip install jupysql jupysql-plugin --quiet
 ```
 
 We continue to work with the Bank and Marketing data set. 
@@ -319,6 +319,12 @@ We'll finish off this section by showing you how you can generate multiple table
 
 Your task now is to create separate tables for each job. This can easily be accomplished using a loop and variable expansion:
 
+```{code-cell} ipython3
+%%sql
+DROP TABLE IF EXISTS services;
+DROP TABLE IF EXISTS management;
+```
+
 ```{code-cell} ipython3
 jobs = ["services", "management"]
 for job in jobs:
@@ -331,6 +337,16 @@ Let's check the tables in our schema:
 %sqlcmd tables
 ```
 
+Delete table
+
+```{code-cell} ipython3
+%%sql
+DROP TABLE bank;
+DROP TABLE services;
+DROP TABLE management;
+```
+
+
 ## Conclusion
 In this module, we have explored how to parameterize SQL queries and effectively integrate with Python for a more interactive data analysis workflow within Jupyter notebooks. This method is facilitated using JupySQL and DuckDB. We've also demonstrated how to dynamically create and use variables within SQL queries, further enhancing the flexibility and interactivity of the notebooks.