diff --git a/episodes/01-short-introduction-to-Python.md b/episodes/01-short-introduction-to-Python.md
index e13dffeaa..408bd03c6 100644
--- a/episodes/01-short-introduction-to-Python.md
+++ b/episodes/01-short-introduction-to-Python.md
@@ -529,7 +529,7 @@ print(rev)
 ```
 
 ```output
-{1: 'one', 2: 'apple-sauce', 3: 'three'}
+{'first': 'one', 'second': 2, 'third': 'three', 2: 'apple-sauce'}
 ```
 
 :::::::::::::::::::::::::::::::::::
diff --git a/episodes/02-starting-with-data.md b/episodes/02-starting-with-data.md
index f2a6eb6db..3e61d0746 100644
--- a/episodes/02-starting-with-data.md
+++ b/episodes/02-starting-with-data.md
@@ -379,7 +379,7 @@ Show that the DataFrame index
 Adapting the name is done with the `rename` function:
 
 ```python
-surveys_df.rename(columns={"plot_id": "plotid"})`)
+surveys_df.rename(columns={"plot_id": "plotid"})
 ```
 
 ::::::::::::::::::::::::::::::::::
diff --git a/episodes/04-data-types-and-format.md b/episodes/04-data-types-and-format.md
index 7a930e9f3..0e60f5caa 100644
--- a/episodes/04-data-types-and-format.md
+++ b/episodes/04-data-types-and-format.md
@@ -195,7 +195,7 @@ print(10/3)
 ```
 
 We can also convert a floating point number to an integer or an integer to
-floating point number. Notice that Python by default rounds down when it
+floating point number. Notice that Python truncates towards zero when it
 converts from floating point to integer.
 
 ```python
@@ -471,7 +471,7 @@ What we've learned:
 
 
 
-[datetime]: https://doc.python.org/2/library/datetime.html
+[datetime]: https://docs.python.org/3/library/datetime.html
 
 
 :::::::::::::::::::::::::::::::::::::::: keypoints
diff --git a/episodes/05-merging-data.md b/episodes/05-merging-data.md
index 5384bb174..e8cb70927 100644
--- a/episodes/05-merging-data.md
+++ b/episodes/05-merging-data.md
@@ -441,10 +441,9 @@ The pandas `merge` function supports other join types:
   rows from the `left` DataFrame without matching join key(s) values are
   discarded.
 - Full (outer) join: Invoked by passing `how='outer'` as an argument. This join
-  type returns the all pairwise combinations of rows from both DataFrames; i.e., the
-  *Cartesian product* and the result DataFrame will use `NaN` where data is missing in one 
-  of the dataframes. This join type is very rarely used, but can be helpful to see all
-  the qualities of both tables, including each common and duplicate column.
+  type keeps all join keys from both DataFrames, combining rows with matching keys.
+  The result DataFrame will use `NaN` where a key has no match in one of the
+  DataFrames. A Cartesian product, in contrast, is produced by `how='cross'`.
 - Self-join: Joins a data frame with itself. Self-joins can be useful when you want to, for
   instance, compare records within the same dataset based on a given criteria. A fuller discussion 
   of how and when it might be useful to do so can be found in [Self-Join and Cross Join in Pandas DataFrame](https://blog.devgenius.io/self-join-and-cross-join-in-pandas-dataframe-b30bfbc0e52a)
diff --git a/episodes/06-loops-and-functions.md b/episodes/06-loops-and-functions.md
index d4a7aaa4b..3f0d6e78d 100644
--- a/episodes/06-loops-and-functions.md
+++ b/episodes/06-loops-and-functions.md
@@ -1065,7 +1065,7 @@ HINT: you can create a loop with a list as follows: `for years in year_list:`
        # Select data for the year
        surveys_year = all_data[all_data.year == this_year]
        if len(surveys_year) == 0: # 'if not len(surveys_year):' will also work 
-           print('no data available for ' + this_year + ', output file not created')
+           print('no data available for ' + str(this_year) + ', output file not created')
        else:
            # Write the new DataFrame to a csv file
            filename = os.path.join(folder_to_save, ''.join([root_name, str(this_year), '.csv']))
@@ -1093,18 +1093,19 @@ HINT: you can create a loop with a list as follows: `for years in year_list:`
        # Select data for the year
        surveys_year = all_data[all_data.year == this_year]
        if len(surveys_year) == 0:
-           print('no data available for ' + this_year + ', output file not created')
+           print('no data available for ' + str(this_year) + ', output file not created')
        else:
-           if folder_to_save not in os.listdir('.'):
+           if not os.path.isdir(folder_to_save):
                os.mkdir(folder_to_save)
                print('Processed directory created')
            # Write the new DataFrame to a csv file
            filename = os.path.join(folder_to_save, ''.join([root_name, str(this_year), '.csv']))
            surveys_year.to_csv(filename)
+           return filename
    ```
    The equivalent, without using `not` to invert the conditional, would be:
    ```python
-   if folder_to_save in os.listdir('.'):
+   if os.path.isdir(folder_to_save):
        pass
    else:
        os.mkdir(folder_to_save)
diff --git a/episodes/07-visualization-ggplot-python.md b/episodes/07-visualization-ggplot-python.md
index 89761d618..7c132eb42 100644
--- a/episodes/07-visualization-ggplot-python.md
+++ b/episodes/07-visualization-ggplot-python.md
@@ -634,7 +634,7 @@ my_plot.save("scatterplot.png", width=10, height=10, dpi=300)
 [plotnine]: https://github.com/has2k1/plotnine?tab=readme-ov-file
 [ggplot2]: https://ggplot2.tidyverse.org
 [grammar-of-graphics]: https://link.springer.com/book/10.1007%2F0-387-28695-0
-[setup-plotnine]: https://datacarpentry.org/python-ecology-lesson/#required-python-packages
+[setup-plotnine]: ../learners/setup.md#required-python-packages
 [plotnine-api]: https://plotnine.org/reference/
 [ggplot2-cheat-sheet]: https://rstudio.github.io/cheatsheets/html/data-visualization.html
 
diff --git a/episodes/08-putting-it-all-together.md b/episodes/08-putting-it-all-together.md
index 24b84018e..197024f36 100644
--- a/episodes/08-putting-it-all-together.md
+++ b/episodes/08-putting-it-all-together.md
@@ -291,7 +291,7 @@ discharge = pd.read_csv("data/bouldercreek_09_2013.txt",
                                "timezone", "flow_rate", "discharge_cd"])
 discharge["datetime"] = pd.to_datetime(discharge["datetime"])
 flood = discharge[(discharge["datetime"] >= "2013-09-11") &
-                  (discharge["datetime"] <= "2013-09-15")]
+                  (discharge["datetime"] < "2013-09-16")]
 
 fig, ax = plt.subplots()
 
diff --git a/episodes/09-working-with-sql.md b/episodes/09-working-with-sql.md
index 05afb3db4..130f0507c 100644
--- a/episodes/09-working-with-sql.md
+++ b/episodes/09-working-with-sql.md
@@ -238,7 +238,7 @@ con.close()
    df1 = pd.read_sql_query('SELECT surveys.year,plots.plot_type,species.genus,species.species,surveys.sex \
        FROM surveys INNER JOIN plots ON surveys.plot = plots.plot_id INNER JOIN species ON \
        surveys.species = species.species_id WHERE surveys.year>=1998 AND surveys.year<=2001 \
-       AND ( surveys.sex = "M" OR surveys.sex = "F")')
+       AND ( surveys.sex = "M" OR surveys.sex = "F")', con)
 
    df1.to_sql("New Table 1", con, if_exists="replace")
    
diff --git a/learners/setup.md b/learners/setup.md
index af499a873..159c27b0a 100644
--- a/learners/setup.md
+++ b/learners/setup.md
@@ -199,7 +199,7 @@ jupyter notebook
 ```
 
 The notebook should open automatically in your browser. If it does not or you
-wish to use a different browser, open this link: [http://localhost:8888](https://localhost:8888).
+wish to use a different browser, open this link: [http://localhost:8888](http://localhost:8888).
 
 ::: prereq :::::
 ## Leave terminal used to launch Jupyter open