diff --git a/.github/workflows/update_build_environment.yml b/.github/workflows/update_build_environment.yml
index 9e555fb2..707fe21a 100644
--- a/.github/workflows/update_build_environment.yml
+++ b/.github/workflows/update_build_environment.yml
@@ -2,6 +2,8 @@ name: Rebuild and publish new ubcdsci/py-intro-to-ds image on DockerHub
 on:
   pull_request:
     types: [opened, synchronize]
+    branches:
+      - 'main'
 
 jobs:
   rebuild-docker:
diff --git a/source/classification1.md b/source/classification1.md
index 471c0ae7..6727e44b 100755
--- a/source/classification1.md
+++ b/source/classification1.md
@@ -16,6 +16,7 @@ kernelspec:
 :tags: [remove-cell]
 from chapter_preamble import *
 from IPython.display import HTML
+from IPython.display import Image
 from sklearn.metrics.pairwise import euclidean_distances
 import numpy as np
 import plotly.express as px
@@ -281,6 +282,7 @@ perimeter and concavity variables. Recall that the default palette in `altair`
 is colorblind-friendly, so we can stick with that here.
 
 ```{code-cell} ipython3
+:tags: ["remove-output"]
 perim_concav = alt.Chart(cancer).mark_circle().encode(
     x=alt.X("Perimeter").title("Perimeter (standardized)"),
     y=alt.Y("Concavity").title("Concavity (standardized)"),
@@ -289,12 +291,16 @@ perim_concav = alt.Chart(cancer).mark_circle().encode(
 perim_concav
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+glue("fig:05-scatter", perim_concav)
+```
+
+:::{glue:figure} fig:05-scatter
 :name: fig:05-scatter
-:figclass: caption-hack
 
 Scatter plot of concavity versus perimeter colored by diagnosis label.
-```
+:::
 
 +++
 
@@ -855,7 +861,11 @@ for neighbor_df in neighbor_df_list:
 # tight layout
 fig.update_layout(margin=dict(l=0, r=0, b=0, t=1), template="plotly_white")
 
-glue("fig:05-more", fig)
+# if HTML, use the plotly 3d image; if PDF, use static image
+if "BOOK_BUILD_TYPE" in os.environ and os.environ["BOOK_BUILD_TYPE"] == "PDF":
+    glue("fig:05-more", Image("img/classification1/plot3d_knn_classification.png"))
+else:
+    glue("fig:05-more", fig)
 ```
 
 ```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
@@ -1432,6 +1442,7 @@ The new imbalanced data is shown in {numref}`fig:05-unbalanced`,
 and we print the counts of the classes using the `value_counts` function.
 
 ```{code-cell} ipython3
+:tags: ["remove-output"]
 rare_cancer = pd.concat((
     cancer[cancer["Class"] == "Benign"],
     cancer[cancer["Class"] == "Malignant"].head(3)
@@ -1445,12 +1456,16 @@ rare_plot = alt.Chart(rare_cancer).mark_circle().encode(
 rare_plot
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+glue("fig:05-unbalanced", rare_plot)
+```
+
+:::{glue:figure} fig:05-unbalanced
 :name: fig:05-unbalanced
-:figclass: caption-hack
 
 Imbalanced data.
-```
+:::
 
 ```{code-cell} ipython3
 rare_cancer["Class"].value_counts()
@@ -1947,16 +1962,15 @@ unscaled_plot + prediction_plot
 ```
 
 ```{code-cell} ipython3
-:tags: [remove-input]
+:tags: [remove-cell]
 glue("fig:05-workflow-plot", (unscaled_plot + prediction_plot))
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+:::{glue:figure} fig:05-workflow-plot
 :name: fig:05-workflow-plot
-:figclass: caption-hack
 
 Scatter plot of smoothness versus area where background color indicates the decision of the classifier.
-```
+:::
 
 +++
 
@@ -1974,6 +1988,7 @@ found in {numref}`Chapter %s <move-to-your-own-machine>`. This will ensure that
 and guidance that the worksheets provide will function as intended.
 
 +++
+
 ## References
 
 ```{bibliography}
diff --git a/source/classification2.md b/source/classification2.md
index 45f18b46..1b0d0fe1 100755
--- a/source/classification2.md
+++ b/source/classification2.md
@@ -395,20 +395,20 @@ the `random_state` argument that is available in many `pandas` and `scikit-learn
 functions. Those functions will then use your `Generator` to generate random numbers instead of
 `numpy`'s default generator. For example, we can reproduce our earlier example by using a `Generator`
 object with the `seed` value set to 1; we get the same lists of numbers once again.
-```{code}
+```python
 from numpy.random import Generator, PCG64
 rng = Generator(PCG64(seed=1))
 random_numbers1_third = nums_0_to_9.sample(n=10, random_state=rng).to_list()
 random_numbers1_third
 ```
-```{code}
+```text
 array([2, 9, 6, 4, 0, 3, 1, 7, 8, 5])
 ```
-```{code}
+```python
 random_numbers2_third = nums_0_to_9.sample(n=10, random_state=rng).to_list()
 random_numbers2_third
 ```
-```{code}
+```text
 array([9, 5, 3, 0, 8, 4, 2, 1, 6, 7])
 ```
 
@@ -432,6 +432,7 @@ You will also notice that we set the random seed using the `np.random.seed` func
 as described in {numref}`randomseeds`.
 
 ```{code-cell} ipython3
+:tags: ["remove-output"]
 # load packages
 import altair as alt
 import pandas as pd
@@ -462,11 +463,18 @@ perim_concav = alt.Chart(cancer).mark_circle().encode(
 perim_concav
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+glue("fig:06-precode", perim_concav)
+```
+
+:::{glue:figure} fig:06-precode
 :name: fig:06-precode
 
 Scatter plot of tumor cell concavity versus smoothness colored by diagnosis label.
-```
+:::
+
+
 
 +++
 
@@ -2205,10 +2213,10 @@ and guidance that the worksheets provide will function as intended.
   text, it requires a bit more mathematical background than we require.
 
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/clustering.md b/source/clustering.md
index f87355db..c44d9e00 100755
--- a/source/clustering.md
+++ b/source/clustering.md
@@ -1063,10 +1063,10 @@ and guidance that the worksheets provide will function as intended.
   learning, it covers *principal components analysis (PCA)*, which is a very
   popular technique for reducing the number of predictors in a data set.
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/img/classification1/plot3d_knn_classification.png b/source/img/classification1/plot3d_knn_classification.png
new file mode 100644
index 00000000..ea9818eb
Binary files /dev/null and b/source/img/classification1/plot3d_knn_classification.png differ
diff --git a/source/img/regression1/plot3d_knn_regression.png b/source/img/regression1/plot3d_knn_regression.png
new file mode 100644
index 00000000..81ea7bb4
Binary files /dev/null and b/source/img/regression1/plot3d_knn_regression.png differ
diff --git a/source/img/regression2/plot3d_linear_regression.png b/source/img/regression2/plot3d_linear_regression.png
new file mode 100644
index 00000000..3cb5ffb9
Binary files /dev/null and b/source/img/regression2/plot3d_linear_regression.png differ
diff --git a/source/inference.md b/source/inference.md
index b280ec0b..ced12c67 100755
--- a/source/inference.md
+++ b/source/inference.md
@@ -645,7 +645,7 @@ was \$`r round(mean(airbnb$price),2)`.
 -->
 
 ```{code-cell} ipython3
-:tags: [remove-input]
+:tags: ["remove-cell"]
 
 glue(
     "fig:11-example-means5",
@@ -681,12 +681,12 @@ glue(
 )
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+:::{glue:figure} fig:11-example-means5
 :name: fig:11-example-means5
-:figclass: caption-hack
 
 Comparison of population distribution, sample distribution, and sampling distribution.
-```
+:::
+
 
 +++
 
@@ -699,7 +699,7 @@ sampling distribution of the sample mean. We indicate the mean of the sampling
 distribution with a vertical line.
 
 ```{code-cell} ipython3
-:tags: [remove-input]
+:tags: ["remove-cell"]
 
 # Plot sampling distributions for multiple sample sizes
 base = alt.Chart(
@@ -753,12 +753,11 @@ glue(
 )
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+:::{glue:figure} fig:11-example-means7
 :name: fig:11-example-means7
-:figclass: caption-hack
 
 Comparison of sampling distributions, with mean highlighted as a vertical line.
-```
+:::
 
 +++
 
@@ -963,8 +962,7 @@ one_sample
 ```
 
 ```{code-cell} ipython3
-:tags: []
-
+:tags: ["remove-output"]
 one_sample_dist = alt.Chart(one_sample).mark_bar().encode(
     x=alt.X("price")
         .bin(maxbins=30)
@@ -975,12 +973,17 @@ one_sample_dist = alt.Chart(one_sample).mark_bar().encode(
 one_sample_dist
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+
+glue("fig:11-bootstrapping1", one_sample_dist)
+```
+
+:::{glue:figure} fig:11-bootstrapping1
 :name: fig:11-bootstrapping1
-:figclass: caption-hack
 
 Histogram of price per night (dollars) for one sample of size 40.
-```
+:::
 
 +++
 
@@ -1002,7 +1005,7 @@ Since we need to sample with replacement when bootstrapping,
 we change the `replace` parameter to `True`.
 
 ```{code-cell} ipython3
-:tags: []
+:tags: ["remove-output"]
 
 boot1 = one_sample.sample(frac=1, replace=True)
 boot1_dist = alt.Chart(boot1).mark_bar().encode(
@@ -1015,12 +1018,17 @@ boot1_dist = alt.Chart(boot1).mark_bar().encode(
 boot1_dist
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+
+glue("fig:11-bootstrapping3", boot1_dist)
+```
+
+:::{glue:figure} fig:11-bootstrapping3
 :name: fig:11-bootstrapping3
-:figclass: caption-hack
 
 Bootstrap distribution.
-```
+:::
 
 ```{code-cell} ipython3
 boot1["price"].mean()
@@ -1055,10 +1063,10 @@ boot20000
 Let's take a look at the histograms of the first six replicates of our bootstrap samples.
 
 ```{code-cell} ipython3
-:tags: []
+:tags: ["remove-output"]
 
 six_bootstrap_samples = boot20000.query("replicate < 6")
-alt.Chart(six_bootstrap_samples, height=150).mark_bar().encode(
+six_bootstrap_fig = alt.Chart(six_bootstrap_samples, height=150).mark_bar().encode(
     x=alt.X("price")
         .bin(maxbins=20)
         .title("Price per night (dollars)"),
@@ -1067,14 +1075,20 @@ alt.Chart(six_bootstrap_samples, height=150).mark_bar().encode(
     "replicate:N",  # Recall that `:N` converts the variable to a categorical type
     columns=2
 )
+six_bootstrap_fig
+```
+
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+
+glue("fig:11-bootstrapping-six-bootstrap-samples", six_bootstrap_fig)
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+:::{glue:figure} fig:11-bootstrapping-six-bootstrap-samples
 :name: fig:11-bootstrapping-six-bootstrap-samples
-:figclass: caption-hack
 
 Histograms of the first six replicates of the bootstrap samples.
-```
+:::
 
 +++
 
@@ -1125,7 +1139,7 @@ boot20000_means
 ```
 
 ```{code-cell} ipython3
-:tags: []
+:tags: ["remove-output"]
 
 boot_est_dist = alt.Chart(boot20000_means).mark_bar().encode(
     x=alt.X("mean_price")
@@ -1137,12 +1151,17 @@ boot_est_dist = alt.Chart(boot20000_means).mark_bar().encode(
 boot_est_dist
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+
+glue("fig:11-bootstrapping5", boot_est_dist)
+```
+
+:::{glue:figure} fig:11-bootstrapping5
 :name: fig:11-bootstrapping5
-:figclass: caption-hack
 
 Distribution of the bootstrap sample means.
-```
+:::
 
 +++
 
@@ -1150,10 +1169,10 @@ Let's compare the bootstrap distribution&mdash;which we construct by taking many
 the true sampling distribution&mdash;which corresponds to taking many samples from the population.
 
 ```{code-cell} ipython3
-:tags: [remove-input]
+:tags: [remove-cell]
 
 sampling_distribution.encoding.x["bin"]["extent"] = (90, 250)
-alt.vconcat(
+bootstr6fig = alt.vconcat(
     alt.layer(
         sampling_distribution,
         alt.Chart(sample_estimates).mark_rule(color="black", size=1.5, strokeDash=[6]).encode(x="mean(mean_price)"),
@@ -1175,12 +1194,19 @@ alt.vconcat(
 )
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+
+glue("fig:11-bootstrapping6", bootstr6fig)
+```
+
+:::{glue:figure} fig:11-bootstrapping6
 :name: fig:11-bootstrapping6
-:figclass: caption-hack
 
 Comparison of the distribution of the bootstrap sample means and sampling distribution.
-```
+:::
+
+
 
 ```{code-cell} ipython3
 :tags: [remove-cell]
@@ -1277,7 +1303,7 @@ the middle 95\% of the sample mean prices in the bootstrap distribution. We can
 visualize the interval on our distribution in {numref}`fig:11-bootstrapping9`.
 
 ```{code-cell} ipython3
-:tags: [remove-input]
+:tags: [remove-cell]
 # Create the annotation for for the 2.5th percentile
 rule_025 = alt.Chart().mark_rule(color="black", size=1.5, strokeDash=[6]).encode(
     x=alt.datum(ci_bounds[0.025])
@@ -1301,15 +1327,22 @@ text_975 = text_025.encode(
 rule_975 = rule_025.encode(x=alt.datum(ci_bounds[0.975]))
 
 # Layer the annotations on top of the distribution plot
-boot_est_dist + rule_025 + text_025 + rule_975 + text_975
+bootstr9fig = boot_est_dist + rule_025 + text_025 + rule_975 + text_975
+```
+
+```{code-cell} ipython3
+:tags: ["remove-cell"]
+
+glue("fig:11-bootstrapping9", bootstr9fig)
 ```
 
-```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
+:::{glue:figure} fig:11-bootstrapping9
 :name: fig:11-bootstrapping9
-:figclass: caption-hack
 
 Distribution of the bootstrap sample means with percentile lower and upper bounds.
-```
+:::
+
+
 
 +++
 
@@ -1365,8 +1398,6 @@ and guidance that the worksheets provide will function as intended.
 
 ## References
 
-+++
-
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/intro.md b/source/intro.md
index 1c2f207d..8c08ade0 100755
--- a/source/intro.md
+++ b/source/intro.md
@@ -367,7 +367,6 @@ and we set `name` to the string `"Alice"`.
 
 ```{code-cell} ipython3
 my_number = 1 + 2
-
 name = "Alice"
 ```
 
@@ -397,12 +396,13 @@ Other symbols won't work since they have their own meanings in Python. For examp
 `-` is the subtraction symbol; if we try to assign a name with
 the `-` symbol, Python will complain and we will get an error!
 
-```
+```{code-cell} ipython3
+:tags: ["remove-output"]
 my-number = 1
 ```
-
-```
-SyntaxError: cannot assign to expression here. Maybe you meant '==' instead of '='?
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print("SyntaxError: cannot assign to expression here. Maybe you meant '==' instead of '='?")
 ```
 
 ```{index} object; naming convention
@@ -705,7 +705,7 @@ The `ten_lang_percent` data frame shows that
 the ten Aboriginal languages in the `ten_lang` data frame were spoken
 as a mother tongue by between 0.008% and 0.18% of the Canadian population.
 
-## Combining analysis steps with chaining and multiline expressions
+## Combining steps with chaining and multiline expressions
 
 It took us 3 steps to find the ten Aboriginal languages most often reported in
 2016 as mother tongues in Canada. Starting from the `can_lang` data frame, we:
@@ -1233,10 +1233,10 @@ make sure to follow the instructions for computer setup
 found in {numref}`Chapter %s <move-to-your-own-machine>`. This will ensure that the automated feedback
 and guidance that the worksheets provide will function as intended.
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/jupyter.md b/source/jupyter.md
index bdd5fd16..928e18a3 100755
--- a/source/jupyter.md
+++ b/source/jupyter.md
@@ -134,7 +134,7 @@ To run a code cell independently, the cell needs to first be activated. This
 is done by clicking on it with the cursor. Jupyter will indicate a cell has been
 activated by highlighting it with a blue rectangle to its left. After the cell
 has been activated ({numref}`activate-and-run-button`), the cell can be run by either pressing
-the **Run** (&blacktriangleright;) button in the toolbar, or by using a keyboard shortcut of
+the **Run** (&#x23F5;) button in the toolbar, or by using a keyboard shortcut of
 `Shift + Enter`.
 
 ```{figure} img/jupyter/activate-and-run-button-annotated.png
@@ -231,7 +231,7 @@ To edit a Markdown cell in Jupyter, you need to double click on the cell. Once
 you do this,  the unformatted (or *unrendered*) version of the text will be
 shown ({numref}`markdown-cell-not-run`). You
 can then use your keyboard to edit the text. To view the formatted
-(or *rendered*) text ({numref}`markdown-cell-run`), click the **Run** (&blacktriangleright;) button in the toolbar,
+(or *rendered*) text ({numref}`markdown-cell-run`), click the **Run** (&#x23F5;) button in the toolbar,
 or use the `Shift + Enter` keyboard shortcut.
 
 ```{figure} img/jupyter/markdown-cell-not-run.png
@@ -285,7 +285,7 @@ As you might know (or at least imagine) by now, Jupyter notebooks are great for
 interactively editing, writing and running Python code; this is what they were
 designed for! Consequently, Jupyter notebooks are flexible in regards to code
 cell execution order. This flexibility means that code cells can be run in any
-arbitrary order using the **Run** (&blacktriangleright;) button. But this flexibility has a downside:
+arbitrary order using the **Run** (&#x23F5;) button. But this flexibility has a downside:
 it can lead to Jupyter notebooks whose code cannot be executed in a linear
 order (from top to bottom of the notebook). A nonlinear notebook is problematic
 because a linear order is the conventional way code documents are run, and
@@ -294,7 +294,7 @@ code is used in some automated process, it will need to run in a linear order,
 from top to bottom of the notebook.
 
 The most common way to inadvertently create a nonlinear notebook is to rely solely
-on using the (&blacktriangleright;) button to execute cells. For example,
+on using the (&#x23F5;) button to execute cells. For example,
 suppose you write some Python code that creates a Python object, say a variable named
 `y`. When you execute that cell and create `y`, it will continue
 to exist until it is deliberately deleted with Python code, or when the Jupyter
@@ -515,3 +515,12 @@ underscore (`_`).
   formatting, two good places to start are CommonMark's [Markdown
   cheatsheet](https://commonmark.org/help/) and [Markdown
   tutorial](https://commonmark.org/help/tutorial/).
+
++++
+
+## References
+
+```{bibliography}
+:filter: docname in docnames
+```
+
diff --git a/source/reading.md b/source/reading.md
index c0f5ec37..e0d4c5f1 100755
--- a/source/reading.md
+++ b/source/reading.md
@@ -109,14 +109,16 @@ So in this case, `happiness_report.csv` would be reached by starting at the root
 then the `dsci-100` folder, then the `project3` folder, and then finally the `data` folder. So its absolute
 path would be `/home/dsci-100/project3/data/happiness_report.csv`. We can load the file using its absolute path
 as a string passed to the `read_csv` function from `pandas`.
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 happy_data = pd.read_csv("/home/dsci-100/project3/data/happiness_report.csv")
 ```
 If we instead wanted to use a relative path, we would need to list out the sequence of steps needed to get from our current
 working directory to the file, with slashes `/` separating each step. Since we are currently in the `project3` folder,
 we just need to enter the `data` folder to reach our desired file. Hence the relative path is `data/happiness_report.csv`,
 and we can load the file using its relative path as a string passed to `read_csv`.
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 happy_data = pd.read_csv("data/happiness_report.csv")
 ```
 Note that there is no forward slash at the beginning of a relative path; if we accidentally typed `"/data/happiness_report.csv"`,
@@ -147,13 +149,13 @@ all of the folders between the computer's root, represented by `/`, and the file
 across different computers. For example, suppose Fatima and Jayden are working on a
 project together on the `happiness_report.csv` data. Fatima's file is stored at
 
-```
+```text
 /home/Fatima/project3/data/happiness_report.csv
 ```
 
 while Jayden's is stored at
 
-```
+```text
 /home/Jayden/project3/data/happiness_report.csv
 ```
 
@@ -275,11 +277,13 @@ With this extra information being present at the top of the file, using
 into Python. In the case of this file, Python just prints a `ParserError`
 message, indicating that it wasn't able to read the file.
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 canlang_data = pd.read_csv("data/can_lang_meta-data.csv")
 ```
-```text
-ParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 6
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print("ParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 6")
 ```
 
 ```{index} ParserError
@@ -841,7 +845,8 @@ be able to connect to a database using this information.
 ```{index} ibis; postgres, ibis; connect
 ```
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 conn = ibis.postgres.connect(
     database="can_mov_db",
     host="fakeserver.stat.ubc.ca",
@@ -859,12 +864,14 @@ connecting to and working with an SQLite database. For example, we can again use
 ```{index} ibis; list_tables
 ```
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 conn.list_tables()
 ```
 
-```text
-["themes", "medium", "titles", "title_aliases", "forms", "episodes", "names", "names_occupations", "occupation", "ratings"]
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print('["themes", "medium", "titles", "title_aliases", "forms", "episodes", "names", "names_occupations", "occupation", "ratings"]')
 ```
 
 We see that there are 10 tables in this database. Let's first look at the
@@ -874,16 +881,20 @@ database.
 ```{index} ibis; table
 ```
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 ratings_table = conn.table("ratings")
 ratings_table
 ```
 
-```text
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print("""
 AlchemyTable: ratings
   title           string
   average_rating  float64
   num_votes       int64
+""")
 ```
 
 ```{index} ibis; []
@@ -892,12 +903,15 @@ AlchemyTable: ratings
 To find the lowest rating that exists in the data base, we first need to
 select the `average_rating` column:
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 avg_rating = ratings_table[["average_rating"]]
 avg_rating
 ```
 
-```text
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print("""
 r0 := AlchemyTable: ratings
   title           string
   average_rating  float64
@@ -906,6 +920,7 @@ r0 := AlchemyTable: ratings
 Selection[r0]
   selections:
     average_rating: r0.average_rating
+""")
 ```
 
 ```{index} database; ordering, ibis; order_by, ibis; head
@@ -914,7 +929,8 @@ Selection[r0]
 Next we use the `order_by` function from `ibis` order the table by `average_rating`,
 and then the `head` function to select the first row (i.e., the lowest score).
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 lowest = avg_rating.order_by("average_rating").head(1)
 lowest.execute()
 ```
@@ -925,7 +941,6 @@ lowest = pd.DataFrame({"average_rating" : [1.0]})
 lowest
 ```
 
-
 We see the lowest rating given to a movie is 1, indicating that it must have
 been a really bad movie...
 
@@ -1250,7 +1265,8 @@ page we want to scrape by providing its URL in quotations to the `requests.get`
 function. This function obtains the raw HTML of the page, which we then
 pass to the `BeautifulSoup` function for parsing:
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 import requests
 import bs4
 
@@ -1338,7 +1354,8 @@ below that `read_html` found 17 tables on the Wikipedia page for Canada.
 ```{index} read function; read_html
 ```
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 canada_wiki_tables = pd.read_html("https://en.wikipedia.org/wiki/Canada")
 len(canada_wiki_tables)
 ```
@@ -1514,7 +1531,8 @@ response using the `json` method.
 
 <!-- we have disabled the below code for reproducibility, with hidden setting
 of the nasa_data object. But you can reproduce this using the DEMO_KEY key -->
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 import requests
 
 nasa_data_single = requests.get(
@@ -1539,7 +1557,8 @@ in an object called `nasa_data`; now the response
 will take the form of a Python list. Each item in the list will correspond to a single day's record (just like the `nasa_data_single` object),
 and there will be 74 items total, one for each day between the start and end dates:
 
-```python
+```{code-cell} ipython3
+:tags: ["remove-output"]
 nasa_data = requests.get(
     "https://api.nasa.gov/planetary/apod?api_key=YOUR_API_KEY&start_date=2023-05-01&end_date=2023-07-13"
     ).json()
@@ -1548,6 +1567,10 @@ len(nasa_data)
 
 ```{code-cell} ipython3
 :tags: [remove-input]
+# need to secretly re-load the nasa data again because the above running code destroys it
+# see PR 341 for why we need to do things this way (essentially due to PDF build)
+with open("data/nasa.json", "r") as f:
+    nasa_data = json.load(f)
 len(nasa_data)
 ```
 
@@ -1626,10 +1649,10 @@ and guidance that the worksheets provide will function as intended.
     - [extracting the data for apartment listings on Craigslist](https://www.youtube.com/embed/YdIWI6K64zo), and
     - [extracting Canadian city names and populations from Wikipedia](https://www.youtube.com/embed/O9HKbdhqYzk).
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/references.bib b/source/references.bib
index e135c672..d306de25 100644
--- a/source/references.bib
+++ b/source/references.bib
@@ -336,7 +336,7 @@ @inproceedings{kluyver2016jupyter
   title={Jupyter Notebooks: a publishing format for reproducible computational workflows},
   author={Kluyver, Thomas and Ragan-Kelley, Benjamin and P{\'e}rez, Fernando and Granger, Brian and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica and Grout, Jason and Corlay, Sylvain and Ivanov, Paul and Avila, Dami{\'a}n and Abdalla, Safia and Willing, Carol and {Jupyter Development Team}},
   year={2016},
-  booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas: Proceedings of the $20^{\text{th}}$ international conference on electronic publishing},
+  booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas: Proceedings of the 20th international conference on electronic publishing},
   publisher = {IOS Press},
   volume = {87},
   address = {Amsterdam}
@@ -508,7 +508,7 @@ @article{requests
 @article{rhoophiuchi,
   title = {Rho Ophiuchi cloud complex},
   author = {{NASA} and {ESA} and {CSA} and {STScI} and {K. Pontoppidan (STScI)} and {A. Pagan (STScI)}},
-  year = {2023},
+  year = {Accessed Online: 2023},
   journal={URL: https://esawebb.org/images/weic2316a/}}
 
 
diff --git a/source/regression1.md b/source/regression1.md
index 87ce1371..a4ed7633 100755
--- a/source/regression1.md
+++ b/source/regression1.md
@@ -20,6 +20,7 @@ kernelspec:
 
 from chapter_preamble import *
 from IPython.display import HTML
+from IPython.display import Image
 import plotly.express as px
 import plotly.graph_objects as go
 ```
@@ -1148,7 +1149,11 @@ fig.update_layout(
     template="plotly_white",
 )
 
-glue("fig:07-knn-mult-viz", fig)
+# if HTML, use the plotly 3d image; if PDF, use static image
+if "BOOK_BUILD_TYPE" in os.environ and os.environ["BOOK_BUILD_TYPE"] == "PDF":
+    glue("fig:07-knn-mult-viz", Image("img/regression1/plot3d_knn_regression.png"))
+else:
+    glue("fig:07-knn-mult-viz", fig)
 ```
 
 ```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
@@ -1208,8 +1213,6 @@ and guidance that the worksheets provide will function as intended.
 
 ## References
 
-+++
-
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/regression2.md b/source/regression2.md
index 67160ece..9db8e2c0 100755
--- a/source/regression2.md
+++ b/source/regression2.md
@@ -20,6 +20,7 @@ kernelspec:
 
 from chapter_preamble import *
 from IPython.display import HTML
+from IPython.display import Image
 import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
@@ -827,7 +828,11 @@ fig.update_layout(
     template="plotly_white",
 )
 
-glue("fig:08-3DlinReg", fig)
+# if HTML, use the plotly 3d image; if PDF, use static image
+if "BOOK_BUILD_TYPE" in os.environ and os.environ["BOOK_BUILD_TYPE"] == "PDF":
+    glue("fig:08-3DlinReg", Image("img/regression2/plot3d_linear_regression.png"))
+else:
+    glue("fig:08-3DlinReg", fig)
 ```
 
 ```{figure} data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7
@@ -1395,10 +1400,10 @@ and guidance that the worksheets provide will function as intended.
   covered earlier are indeed more flexible but become very slow when given lots
   of data.
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/setup.md b/source/setup.md
index 4ef17991..c45a0980 100755
--- a/source/setup.md
+++ b/source/setup.md
@@ -195,7 +195,8 @@ found in [the online Docker documentation](https://docs.docker.com/desktop/insta
 ### Ubuntu
 
 **Installation** To install Docker on Ubuntu, open the terminal and enter the following five commands.
-```
+```{code-cell}
+:tags: ["remove-output"]
 sudo apt update
 sudo apt install ca-certificates curl gnupg
 curl -fsSL https://get.docker.com -o get-docker.sh
@@ -207,7 +208,8 @@ sudo sh get-docker.sh
 and look for the line `FROM ubcdsci/py-dsci-100:` followed by a tag consisting of a sequence of numbers and letters.
 Then in the terminal, navigate to the directory where you want to run JupyterLab, and run
 the following command, replacing `TAG` with the *tag* you found earlier.
-```
+```{code-cell}
+:tags: ["remove-output"]
 docker run --rm -v $(pwd):/home/jovyan/work -p 8888:8888 ubcdsci/py-dsci-100:TAG jupyter lab
 ```
 The terminal will then print some text as the Docker container starts. Once the text stops scrolling, find the
@@ -283,7 +285,8 @@ A JupyterLab Desktop session, showing the Terminal option at the bottom.
 
 
 In this terminal, run the following commands:
-```
+```{code-cell}
+:tags: ["remove-output"]
 pip install --upgrade jupyterlab-git
 conda env update --file https://raw.githubusercontent.com/UBC-DSCI/data-science-a-first-intro-python-worksheets/main/environment.yml
 ```
@@ -304,7 +307,8 @@ correctly set up and ready for use.
 Open the terminal ([how-to video](https://youtu.be/5AJbWEWwnbY))
 and type the following command:
 
-```
+```{code-cell}
+:tags: ["remove-output"]
 xcode-select --install
 ```
 Next, visit the ["Installation" section of the JupyterLab Desktop homepage](https://github.com/jupyterlab/jupyterlab-desktop#installation).
@@ -326,18 +330,21 @@ the various Python software packages needed for the worksheets.
 
 **Installation** First, we will install Git for version control.
 Open the terminal and type the following commands:
-```
+```{code-cell}
+:tags: ["remove-output"]
 sudo apt update
 sudo apt install git
 ```
 Next, visit the ["Installation" section of the JupyterLab Desktop homepage](https://github.com/jupyterlab/jupyterlab-desktop#installation).
 Download the `JupyterLab-Setup-Debian.deb` installer file for Ubuntu/Debian.
 Open a terminal, navigate to where the installer file was downloaded, and run the command
-```
+```{code-cell}
+:tags: ["remove-output"]
 sudo dpkg -i JupyterLab-Setup-Debian.deb
 ```
 Run JupyterLab Desktop using the command
-```
+```{code-cell}
+:tags: ["remove-output"]
 jlab
 ```
 
diff --git a/source/version-control.md b/source/version-control.md
index 941ee49d..48c49026 100755
--- a/source/version-control.md
+++ b/source/version-control.md
@@ -1130,10 +1130,10 @@ you can expand your knowledge through the resources listed below:
   is an excellent additional resource to consult if you need help
   generating and using personal access tokens.
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/viz.md b/source/viz.md
index f82d85c9..e062a679 100755
--- a/source/viz.md
+++ b/source/viz.md
@@ -851,8 +851,8 @@ and is just added for readability.
 
 ```{code-cell} ipython3
 canadian_population = 35_151_728
-can_lang["mother_tongue_percent"] = can_lang["mother_tongue"] / canadian_population * 100
-can_lang["most_at_home_percent"] = can_lang["most_at_home"] / canadian_population * 100
+can_lang["mother_tongue_percent"] = can_lang["mother_tongue"]/canadian_population*100
+can_lang["most_at_home_percent"] = can_lang["most_at_home"]/canadian_population*100
 can_lang[["mother_tongue_percent", "most_at_home_percent"]]
 ```
 
@@ -2063,10 +2063,10 @@ and guidance that the worksheets provide will function as intended.
   is where you should look if you want to learn about `date` and `time`, including
   how to create them, and how to use them to effectively handle durations, etc
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```
diff --git a/source/wrangling.md b/source/wrangling.md
index 998f6bc0..aa5fe42d 100755
--- a/source/wrangling.md
+++ b/source/wrangling.md
@@ -1490,8 +1490,9 @@ in our case, we get an error.
 region_lang["most_at_home":"lang_known"].groupby("region").max()
 ```
 
-```
-KeyError: "region"
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print('KeyError: "region"')
 ```
 
 This is because when we use `[]` we selected only the columns between
@@ -1727,23 +1728,20 @@ Instead of using the `assign` method we can directly modify the `english_lang` d
 This would be a more natural choice in this particular case,
 since the syntax is more convenient for simple column modifications and additions.
 ```{code-cell} ipython3
-:tags: [remove-cell]
-english_lang["city_pops"] = [4098927, 5928040, 1392609, 1321426, 2463431]
-```
-```python
+:tags: [remove-output]
 english_lang["city_pops"] = [4098927, 5928040, 1392609, 1321426, 2463431]
 english_lang
 ```
-```text
+```{code-cell} ipython3
+:tags: ["remove-input"]
+print("""
 /tmp/ipykernel_12/2654974267.py:1: SettingWithCopyWarning:
 A value is trying to be set on a copy of a slice from a DataFrame.
 Try using .loc[row_indexer,col_indexer] = value instead
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
   english_lang["city_pops"] = [4098927, 5928040, 1392609, 1321426, 2463431]
-```
-```{code-cell} ipython3
-:tags: [remove-input]
+""")
 english_lang
 ```
 
@@ -1910,10 +1908,10 @@ and guidance that the worksheets provide will function as intended.
   what you want. In that case, you may consider using [a for loop](https://wesmckinney.com/book/python-basics.html#control_for) {cite:p}`mckinney2012python`.
 
 
-## References
-
 +++
 
+## References
+
 ```{bibliography}
 :filter: docname in docnames
 ```