diff --git a/environment.yml b/environment.yml
index 7852bcbf..219188ce 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,8 +2,8 @@ name: lecture-datascience
 channels:
   - default
 dependencies:
-  - python=3.8
-  - anaconda=2021.05
+  - python=3.9
+  - anaconda=2021.11
   - pip
   - pip:
     - jupyter-book>=0.12.1,<0.13
@@ -13,7 +13,6 @@ dependencies:
     - joblib
     - interpolation
     - sphinx-tojupyter
-    - fiona 
     - geopandas 
     - pyLDAvis >= 3.3.0
     - gensim 
@@ -38,3 +37,6 @@ dependencies:
     - lxml
   - conda:
     - python-graphviz
+    - fiona
+    - llvmlite
+    - nltk
diff --git a/lectures/_config.yml b/lectures/_config.yml
index 2eb21f74..66c1d1dc 100644
--- a/lectures/_config.yml
+++ b/lectures/_config.yml
@@ -11,7 +11,7 @@ html:
   baseurl: https://datascience.quantecon.org/
 
 sphinx:
-  extra_extensions: [sphinx_multitoc_numbering, sphinx_exercise, sphinx_tojupyter]
+  extra_extensions: [sphinx_multitoc_numbering, sphinx_tojupyter]
   config:
     html_favicon: _static/lectures-favicon.ico
     html_static_path: ['_static']
diff --git a/lectures/pandas/merge.md b/lectures/pandas/merge.md
index 7f67d946..15602fef 100644
--- a/lectures/pandas/merge.md
+++ b/lectures/pandas/merge.md
@@ -660,7 +660,7 @@ avg_delays
 The one issue with this dataset is that we don't know what all those two
 letter carrier codes are!
 
-Thankfully, we have a second dataset that maps the two letter code
+Thankfully, we have a second dataset that maps the two-letter code
 into the full airline name.
 
 ```{code-cell} python
@@ -673,7 +673,7 @@ Let's merge these names so we know which airlines we should avoid
 flying...
 
 ```{code-cell} python
-avg_delays_w_code = avg_delays.join(carrier_code)
+avg_delays_w_code = avg_delays.join(carrier_code.set_index("Code"))
 avg_delays_w_code.sort_values("CarrierDelay", ascending=False)
 ```