Hello,

First, thank you so much for these great tutorials. There are a number of warnings regarding the usage of "just the indexing operator" for quite a while and the explanation of .loc and .iloc were tremendously helpful.

I'm writing to recommend that you add an example of assigning a new column from a boolean selection that returns a boolean series in the article on assignment. Take for example, the following:

criteria = df[‘some_col’] > sum_number
criteria.head()

0     True
1    False
2     True
4     True
6    False

Using just the assignment operator...

df['new_col'] = df['some-col'] > some_number

...works but yields the warning:

Try using .loc[row_indexer,col_indexer] = value instead

The closest example I've found in your article is this one:

last_name = pd.Series(data=['Smith', 'Jones', 'Williams', 'Green', 'Brown', 'Simpson', 'Peters'],
                      index=['Tom', 'Niko', 'Penelope', 'Aria', 'Sofia', 'Dean', 'Zach'])
last_name
df['last_name'] = last_name

However, at least in Pandas 0.19.2, this will still yield the same error. After searching around a bit I found this stack overflow discussion which states that after Pandas 0.16.0, the best way to do this is to use the assign function in the following manner:

criteria = df[‘some_col’] > sum_number
df_three.assign(new_col_name = criteria) #note: no quotes on new_col_name

Which seems to work well for me.

Alternatively, I suppose you can simply add which version the tutorial was written under.

Thanks again for this wonderful guide!

great read!
fix .loc --> .iloc in line 4596

Thank you for the tutorials!
I think there's a typo in Exercise 13 of 02 Boolean Indexing.ipynb: it reads "Get the same exact result as exercise 11", but probably should read "Get the same exact result as exercise 12".