lesson 3 proposal

Author: caesoma

_episodes/03-index-slice-subset.md

@@ -118,7 +118,7 @@ the related Python data type dictionary).
 > the names of built-in data structures and methods. For example, a _list_ is a built-in
 > data type. It is possible to use the word 'list' as an identifier for a new object,
 > for example `list = ['apples', 'oranges', 'bananas']`. However, you would then
-> be unable to create an empty list using `list()` or convert a tuple to a 
+> be unable to create an empty list using `list()` or convert a tuple to a
 > list using `list(sometuple)`.
 {: .callout}
 
@@ -358,22 +358,28 @@ gives the **output**
 Remember that Python indexing begins at 0. So, the index location [2, 6]
 selects the element that is 3 rows down and 7 columns over in the DataFrame.
 
+It is worth noting that rows are selected when using `loc` with a single list of labels (or `iloc` with a single list of integers). However, unlike `loc` or `iloc`, indexing a data frame directly with labels will select columns, while ranges of integers will select rows. Direct indexing of rows is redundant with using `iloc`, and will raise a `KeyError` if a single integer or list is used; the error will also occur if index labels are used without `loc` (or column labels used with it).
+A useful rule of thumb is the following: integer-based slicing is best done with `iloc` and will avoid errors (and is generally consistent with indexing of Numpy arrays), label-based slicing of rows is done with `loc`, and slicing of columns by directly indexing column names.
 
 
 > ## Challenge - Range
 >
 > 1. What happens when you execute:
 >
 >    - `surveys_df[0:1]`
+>    - `surveys_df[0]`
 >    - `surveys_df[:4]`
 >    - `surveys_df[:-1]`
 >
 > 2. What happens when you call:
 >
+>    - `surveys_df.iloc[0:1]`
+>    - `surveys_df.iloc[0]`
+>    - `surveys_df.iloc[:4, :]`
 >    - `surveys_df.iloc[0:4, 1:4]`
 >    - `surveys_df.loc[0:4, 1:4]`
 >
-> - How are the two commands different?
+> - How are the last two commands different?
 {: .challenge}