Refactor and clean up the data downloading

Author: choldgraf

book/cloud.md

@@ -361,7 +361,7 @@ df_clusters = df_clusters.query("users > 0")
 df_clusters["logusers"] = df_clusters["users"].map(np.log10)
 
 # List of clusters sorted by size
-sorted_clusters = df_clusters.groupby("cluster")["users"].mean().sort_values("users", ascending=False).index.values
+sorted_clusters = df_clusters.groupby("cluster")["users"].mean().sort_values(ascending=False).index.values
 ```
 
 `````{code-cell} ipython3

book/finances.md

@@ -364,7 +364,7 @@ for fig in figures.values():
     fig.show()
 ```
 
-(undertanding-revenue)=
+(understanding-revenue)=
 ## Understanding revenue
 
 The plots above show our revenue projections under different assumptions, going 18 months into the future:

book/scripts/download_airtable_data.py

@@ -33,14 +33,10 @@
 
 # These correspond to AirTable URLs of the form:
 #   airtable.com/{{ BASE ID }}/{{ TABLE ID }}/{{VIEW ID}}
+# Only downloading tables that are actually used in the book (cloud.md)
 views = [
     ("communities", "appbjBTRIbgRiElkr", "tblYGygEo5PQBSUYt", "viw2F6xVWJujWKCuj"),
     ("locations", "appbjBTRIbgRiElkr", "tblNiMH0gYRVhVdhE", "viwYjmYFRWWJnrv8Y"),
-    # ("accounting", "appbjBTRIbgRiElkr", "tblNjmVbPaVmC7wc3", "viw1daKSu2dTcd5lg"),  # This table was deleted, so removing it for now
-    ("contracts", "appbjBTRIbgRiElkr", "tbliwB70vYg3hlkb1", "viwWPJhcFbXUJZUO6"),
-    ("leads", "appbjBTRIbgRiElkr", "tblmRU6U53i8o7z2I", "viw8xzzSXk8tPwBho"),
-    ("opportunities", "appbjBTRIbgRiElkr", "tblBTPDI1nKoq8wOL", "viwuJxmlTnW1VZxIm"),
-    ("sales", "appbjBTRIbgRiElkr", "tblBTPDI1nKoq8wOL", "viwcsrE83taP6GhSl"),
 ]
 
 ## Load in airtable