From 87fc199b22879e5188024b7e020008075d62b504 Mon Sep 17 00:00:00 2001
From: Paul Cannon <102183179+pcc-git@users.noreply.github.com>
Date: Mon, 22 Jan 2024 16:12:20 -0700
Subject: [PATCH] Update _index.md

---
 content/Slides/p1/d3/_index.md | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/content/Slides/p1/d3/_index.md b/content/Slides/p1/d3/_index.md
index 8d9df73..930c751 100644
--- a/content/Slides/p1/d3/_index.md
+++ b/content/Slides/p1/d3/_index.md
@@ -60,15 +60,6 @@ names.describe()
 
 **What types of charts are there?**
 
-```python
-alt.Chart()
-   .mark_*()
-   .encode()
-
-# alt.Chart().encode()
-   
-```
-
 <br>
 
 **What data do you need to make that chart?**
@@ -85,6 +76,7 @@ kobe2 = names.query("name == 'Kobe'").filter(items=["name", "year", "Total"])
 
 
 <br>
+<!--------------------
 
 {{< faq "Work with your partner to create a line chart that includes both of your names?">}}
 
@@ -112,7 +104,6 @@ Remember this advice [from Edward Tufte](https://medium.com/@AnyChart/advices-by
 > To be truthful and revealing, data graphics must bear on the question at the heart of quantitative thinking: "Compared to what?" The emaciated, data-thin design should always provoke suspicion, for graphics often lie by omission, leaving out data sufficient for comparisons.
 
 
-<!--------------------
 {{< faq "What are some charts types we could use to answer this question?">}}
 
 __There is a clear first choice, but I think there are a few other choices that could provide insight.__