corrections ch02

02-attribute-operations.qmd

@@ -220,11 +220,11 @@ world[world['continent'].isin(['North America', 'South America'])]  \
 
 Aggregation involves summarizing data based on one or more *grouping variables* (typically values in a column; geographic aggregation is covered in @sec-vector-spatial-aggregation).
 A classic example of this attribute-based aggregation is calculating the number of people per continent based on country-level data (one row per country).
-The `world` dataset contains the necessary ingredients: the columns `pop` and `continent`, the population and the grouping variable, respectively.
+The `world` dataset contains the necessary ingredients: the columns `pop` and `continent`, the target variable and the grouping variable, respectively.
 The aim is to find the `sum()` of country populations for each continent, resulting in a smaller table or vector layer (of continents).
 Since aggregation is a form of data reduction, it can be a useful early step when working with large datasets.
 
-Attribute-based aggregation can be achieved using a combination of `.groupby` and `.sum` (package **pandas**), where the former groups the data by the grouping variable(s) and the latter calculates the sum of the specified column(s). The `.reset_index` methods moves the grouping variable into an ordinary column, rather than an index (the default), which is something we typically want to do.
+Attribute-based aggregation can be achieved using a combination of `.groupby` and `.sum` (package **pandas**), where the former groups the data by the grouping variable(s) and the latter calculates the sum of the specified column(s). The `.reset_index` method moves the grouping variable into an ordinary column, rather than an index (the default), which is something we typically want to do.
 
 ```{python}
 world_agg1 = world.groupby('continent')[['pop']].sum().reset_index()
@@ -254,7 +254,7 @@ fig, ax = plt.subplots(figsize=(6, 3))
 world_agg2.plot(column='pop', edgecolor='black', legend=True, ax=ax);
 ```
 
-Other options for the `aggfunc` parameter in `.dissolve` [include](https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html)`'first'`, `'last'`, `'min'`, `'max'`, `'sum'`, `'mean'`, `'median'`.
+Other options for the `aggfunc` parameter in `.dissolve` [include](https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html) `'first'`, `'last'`, `'min'`, `'max'`, `'sum'`, `'mean'`, `'median'`.
 Additionally, we can pass custom functions here.
 
 As a more complex example, the following code shows how we can calculate the total population, area, and count of countries, per continent.
@@ -300,7 +300,7 @@ In the following code example, given the `world_agg3` continent summary (@fig-sp
 -   drop the geometry column,
 -   calculate population density of each continent,
 -   arrange continents by the number of countries each contains, and
--   keep only the 3 most populous continents.
+-   keep only the 3 most country-rich continents.
 
 ```{python}
 world_agg4 = world_agg3.drop(columns=['geometry'])