Spread out exercises in each episode

episodes/conditionals.md

@@ -76,6 +76,52 @@ for checkout in checkouts:
 
 Notice that our `else` statement led to a false output that says 10 is under the limit. We can address this by adding a different kind of `else` statement.
 
+:::::::::::::::::::::::::::::::::::::::  challenge
+
+## Age conditionals
+
+Write a Python program that checks the age of a user to determine if they will receive a youth or adult library card. The program should:
+
+1. Store `age` in a variable.
+2. Use an `if` statement to check if the age is 16 or older. If true, print "You are eligible for an adult library card."
+3. Use an `else` statement to print "You are eligible for a youth library card" if the age is less than 16.
+
+If you finish early, try this challenge: 
+
+- In a new cell, adapt your program to loop through a list of age values, testing each age with the same output as above.
+
+:::::::::::::::  solution
+
+## Solution
+
+For parts 1 to 3:
+
+```python
+age = 25
+
+if age >= 16:
+  print('You are eligible for an adult library card.')
+else:
+  print('You are eligible for a youth library card.')
+```
+
+For the challenge:
+```python
+ages = [10, 16, 30, 65]
+
+for age in ages:
+  if age >= 16:
+    print('You are eligible for an adult library card.')
+  else:
+    print('You are eligible for a youth library card.')
+```
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
 ## Use `elif` to specify additional tests.
 
 You can use `elif` (short for "else if") to provide several alternative choices, each with its own test. An `elif` statement should always be associated with an `if` statement, and must come before the `else` statement (which is the catch all).
@@ -153,52 +199,6 @@ for user in users:
 *Warning*: 120 is over the grad limit.
 ```
 
-:::::::::::::::::::::::::::::::::::::::  challenge
-
-## Age conditionals
-
-Write a Python program that checks the age of a user to determine if they will receive a youth or adult library card. The program should:
-
-1. Store `age` in a variable.
-2. Use an `if` statement to check if the age is 16 or older. If true, print "You are eligible for an adult library card."
-3. Use an `else` statement to print "You are eligible for a youth library card" if the age is less than 16.
-
-If you finish early, try this challenge: 
-
-- In a new cell, adapt your program to loop through a list of age values, testing each age with the same output as above.
-
-:::::::::::::::  solution
-
-## Solution
-
-For parts 1 to 3:
-
-```python
-age = 25
-
-if age >= 16:
-  print('You are eligible for an adult library card.')
-else:
-  print('You are eligible for a youth library card.')
-```
-
-For the challenge:
-```python
-ages = [10, 16, 30, 65]
-
-for age in ages:
-  if age >= 16:
-    print('You are eligible for an adult library card.')
-  else:
-    print('You are eligible for a youth library card.')
-```
-
-
-:::::::::::::::::::::::::
-
-::::::::::::::::::::::::::::::::::::::::::::::::::
-
-
 :::::::::::::::::::::::::::::::::::::::  challenge
 
 ## Conditional logic: Fill in the blanks

episodes/data-visualisation.md

@@ -138,55 +138,6 @@ albany['circulation'].plot(kind='hist', bins=20,
 
 ![](fig/albany-circ-hist-9.png){alt="histogram of the Albany branch circulation."}
 
-## Use Plotly for interactive plots 
-
-Let’s switch back to the full DataFrame in `df_long` and use another
-plotting package in Python called Plotly. 
-
-```python
-import plotly.express as px
-```
-
-Now we can visualize how circulation counts have changed over time for selected branches. This can be especially useful for identifying trends, seasonality, or data anomalies. We willfirst create a subset of our data to look at branches starting with the letter 'A'. Feel free to select different branches. After subsetting, we will sort our new DataFrame by date and then plot our data by date and circulation count.
-
-``` python
-# Creating a line plot for a few selected branches to avoid clutter
-selected_branches = df_long[df_long['branch'].isin(['Altgeld',
- 'Archer Heights',
- 'Austin',
- 'Austin-Irving',
- 'Avalon'])]
-selected_branches = selected_branches.sort_values(by='date')
-```
-
-``` python
-fig = px.line(selected_branches, x=selected_branches.index, y='circulation', color='branch', title='Circulation Over Time for Selected Branches')
-fig.show()
-```
-
-Here is a view of the [interactive output of the Plotly line chart](learners/line_plot_int.html).  
-
-
-One advantage that Plotly provides over Matplotlib is that it has some interactive features out of the box. Hover your cursor over the lines in the output to find out more granular data about specific branches over time.
-
-
-### Bar plots with Plotly
-
-Let’s use a barplot to compare the distribution of circulation counts
-among branches. We first need to group our data by branch and sum up the circulation counts. Then we can use the bar plot to show the
-distribution of total circulation over branches.
-
-``` python
-# Aggregate circulation by branch
-total_circulation_by_branch = df_long.groupby('branch')['circulation'].sum().reset_index()
-
-# Create a bar plot
-fig = px.bar(total_circulation_by_branch, x='branch', y='circulation', title='Total Circulation by Branch')
-fig.show()
-```
-
-Here is a view of the [interactive output of the Plotly bar chart](learners/bar_plot_int.html).  
-
 :::::::::::::::::::::::::::::::::::::::  challenge
 
 ## Plotting with Pandas
@@ -248,6 +199,56 @@ uptown['circulation'].plot(title='Uptown Circulation',
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
+
+## Use Plotly for interactive plots 
+
+Let’s switch back to the full DataFrame in `df_long` and use another
+plotting package in Python called Plotly. 
+
+```python
+import plotly.express as px
+```
+
+Now we can visualize how circulation counts have changed over time for selected branches. This can be especially useful for identifying trends, seasonality, or data anomalies. We willfirst create a subset of our data to look at branches starting with the letter 'A'. Feel free to select different branches. After subsetting, we will sort our new DataFrame by date and then plot our data by date and circulation count.
+
+``` python
+# Creating a line plot for a few selected branches to avoid clutter
+selected_branches = df_long[df_long['branch'].isin(['Altgeld',
+ 'Archer Heights',
+ 'Austin',
+ 'Austin-Irving',
+ 'Avalon'])]
+selected_branches = selected_branches.sort_values(by='date')
+```
+
+``` python
+fig = px.line(selected_branches, x=selected_branches.index, y='circulation', color='branch', title='Circulation Over Time for Selected Branches')
+fig.show()
+```
+
+Here is a view of the [interactive output of the Plotly line chart](learners/line_plot_int.html).  
+
+
+One advantage that Plotly provides over Matplotlib is that it has some interactive features out of the box. Hover your cursor over the lines in the output to find out more granular data about specific branches over time.
+
+
+### Bar plots with Plotly
+
+Let’s use a barplot to compare the distribution of circulation counts
+among branches. We first need to group our data by branch and sum up the circulation counts. Then we can use the bar plot to show the
+distribution of total circulation over branches.
+
+``` python
+# Aggregate circulation by branch
+total_circulation_by_branch = df_long.groupby('branch')['circulation'].sum().reset_index()
+
+# Create a bar plot
+fig = px.bar(total_circulation_by_branch, x='branch', y='circulation', title='Total Circulation by Branch')
+fig.show()
+```
+
+Here is a view of the [interactive output of the Plotly bar chart](learners/bar_plot_int.html).  
+
 :::::::::::::::::::::::::::::::::::::::  challenge
 
 ## Plot the top five branches

episodes/for-loops.md

@@ -163,6 +163,38 @@ for number in range(0,3):
 2
 ```
 
+:::::::::::::::::::::::::::::::::::::::  challenge
+
+## Use range() in a loop
+
+Print out the numbers 10, 11, 12, 13, 14, 15, using range() in a `for` loop.
+
+:::::::::::::::  solution
+
+## Solution
+
+```python
+
+for num in range(10, 16):
+    print(num)
+
+```
+
+```output
+10
+11
+12
+13
+14
+15
+```
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
 ## Accumulators
 
 A common loop pattern is to initialize an *accumulator* variable to zero, an empty string, or an empty list before the loop begins. Then the loop updates the accumulator variable with values from a collection.
@@ -249,37 +281,6 @@ for veg in vegetables:
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 :::::::::::::::::::::::::::::::::::::::  challenge
 
-## Use range() in a loop
-
-Print out the numbers 10, 11, 12, 13, 14, 15, using range() in a `for` loop.
-
-:::::::::::::::  solution
-
-## Solution
-
-```python
-
-for num in range(10, 16):
-    print(num)
-
-```
-
-```output
-10
-11
-12
-13
-14
-15
-```
-
-
-:::::::::::::::::::::::::
-
-::::::::::::::::::::::::::::::::::::::::::::::::::
-
-:::::::::::::::::::::::::::::::::::::::  challenge
-
 ## Use a string index in a loop
 
 How would you loop through a list with the values 'red', 'green', and 'blue' to create the acronym `rgb`, pulling from the first letters in each string? Print the acronym when the loop is finished.

episodes/getting-started.md

@@ -18,7 +18,7 @@ exercises: 5
 
 - How can I identify and use key features of JupyterLab to create and manage a Python notebook?
 - How do I run Python code in JupyterLab, and how can I see and interpret the results?
-- 
+
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Why Python?
@@ -151,7 +151,7 @@ If you move your cursor back to the first cell, just after the `7 * 3` code, and
 
 ```python
 7 * 3
-2 +1
+2 + 1
 ```
 
 While Python runs both calculations Juypter will only display the output from the last line of code in a specific cell, unless you tell it to do otherwise.