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@@ -365,6 +365,8 @@ You can install [QuantEcon.py](http://quantecon.org/quantecon-py) by
 starting Jupyter and typing
 
 ```{code-block} ipython3
+:class: no-execute
+
 !conda install quantecon
 ```
 
@@ -373,6 +375,8 @@ into a cell.
 Alternatively, you can type the following into a terminal
 
 ```{code-block} bash
+:class: no-execute
+
 conda install quantecon
 ```
 
@@ -381,6 +385,8 @@ More instructions can be found on the [library page](http://quantecon.org/quante
 To upgrade to the latest version, which you should do regularly, use
 
 ```{code-block} bash
+:class: no-execute
+
 conda upgrade quantecon
 ```
 
@@ -389,6 +395,8 @@ Another library we will be using is [interpolation.py](https://github.com/EconFo
 This can be installed by typing in Jupyter
 
 ```{code-block} ipython3
+:class: no-execute
+
 !conda install -c conda-forge interpolation
 ```
 
@@ -517,6 +525,8 @@ As the 1st task, try
 For example, if you've installed the command line version, open up a terminal and enter.
 
 ```{code-block} bash
+:class: no-execute
+
 git clone https://github.com/QuantEcon/QuantEcon.py
 ```
 
 
@@ -198,6 +198,8 @@ Compiled languages avoid these overheads with explicit, static types.
 For example, consider the following C code, which sums the integers from 1 to 10
 
 ```{code-block} c
+:class: no-execute
+
 #include <stdio.h>
 
 int main(void) {
 
@@ -143,6 +143,8 @@ All iterators can be placed to the right of the `in` keyword in `for` loop state
 In fact this is how the `for` loop works:  If we write
 
 ```{code-block} python3
+:class: no-execute
+
 for x in iterator:
     <code block>
 ```
@@ -156,6 +158,8 @@ then the interpreter
 So now you know how this magical looking syntax works
 
 ```{code-block} python3
+:class: no-execute
+
 f = open('somefile.txt', 'r')
 for line in f:
     # do something
@@ -527,6 +531,8 @@ We are now working in the module `__main__`, and hence the namespace for `__main
 Next, we import a module called `amodule`
 
 ```{code-block} python3
+:class: no-execute
+
 import amodule
 ```
 
@@ -1661,6 +1667,8 @@ Write a function to recursively compute the $t$-th Fibonacci number for any $t$.
 Complete the following code, and test it using [this csv file](https://raw.githubusercontent.com/QuantEcon/lecture-python-programming/master/source/_static/lecture_specific/python_advanced_features/test_table.csv), which we assume that you've put in your current working directory
 
 ```{code-block} python3
+:class: no-execute
+
 def column_iterator(target_file, column_number):
     """A generator function for CSV files.
     When called with a file name target_file (string) and column number
@@ -1681,6 +1689,8 @@ for date in dates:
 Suppose we have a text file `numbers.txt` containing the following lines
 
 ```{code-block} none
+:class: no-execute
+
 prices
 3
 8
 
@@ -145,6 +145,8 @@ easily enough if you look around.
 On this machine, it's located in
 
 ```{code-block} ipython
+:class: no-execute
+
 anaconda3/lib/python3.7/site-packages/numpy
 ```
 
@@ -334,6 +336,8 @@ This example helps to clarify how the `for` loop works:  When we execute a
 loop of the form
 
 ```{code-block} python3
+:class: no-execute
+
 for variable_name in sequence:
     <code block>
 ```
 
@@ -315,6 +315,8 @@ Note that if `newfile.txt` is not in the present working directory then this cal
 In this case, you can shift the file to the pwd or specify the [full path](https://en.wikipedia.org/wiki/Path_%28computing%29) to the file
 
 ```{code-block} python3
+:class: no-execute
+
 f = open('insert_full_path_to_file/newfile.txt', 'r')
 ```
 
@@ -621,6 +623,8 @@ f?
 ```
 
 ```{code-block} ipython
+:class: no-execute
+
 Type:       function
 String Form:<function f at 0x2223320>
 File:       /home/john/temp/temp.py
@@ -633,6 +637,8 @@ f??
 ```
 
 ```{code-block} ipython
+:class: no-execute
+
 Type:       function
 String Form:<function f at 0x2223320>
 File:       /home/john/temp/temp.py
@@ -693,6 +699,8 @@ Here the function created by `lambda` is said to be *anonymous* because it was n
 In a {ref}`previous lecture <python_by_example>`, you came across the statement
 
 ```{code-block} python3
+:class: no-execute
+
 plt.plot(x, 'b-', label="white noise")
 ```
 
 
@@ -777,6 +777,8 @@ Implement $F_n$ as a class called `ECDF`, where
 Your code should work as follows (modulo randomness)
 
 ```{code-block} python3
+:class: no-execute
+
 from random import uniform
 
 samples = [uniform(0, 1) for i in range(10)]
@@ -785,6 +787,8 @@ F(0.5)  # Evaluate ecdf at x = 0.5
 ```
 
 ```{code-block} python3
+:class: no-execute
+
 F.observations = [uniform(0, 1) for i in range(1000)]
 F(0.5)
 ```