Merge pull request #268 from UBC-DSCI/from-import

From import

Author: trevorcampbell

source/classification1.md

@@ -869,6 +869,15 @@ in the `scikit-learn` package (named `sklearn` in Python) will help keep our cod
 less we have to code ourselves, the fewer mistakes we will likely make. 
 Before getting started with $K$-nearest neighbors, we need to tell the `sklearn` package 
 that we prefer using `pandas` data frames over regular arrays via the `set_config` function. 
+```{note}
+You will notice a new way of importing functions in the code below: `from ... import ...`. This lets us
+import *just* `set_config` from `sklearn`, and then call `set_config` without any package prefix.
+We will import functions using `from` extensively throughout
+this and subsequent chapters to avoid very long names from `scikit-learn`
+that clutter the code
+(like `sklearn.neighbors.KNeighborsClassifier`, which has 38 characters!).
+```
+
 ```{code-cell} ipython3
 from sklearn import set_config
 

source/reading.md

@@ -1268,7 +1268,7 @@ no_official_lang_data.to_csv("data/no_official_languages.csv", index=False)
 %
 % ```{code-cell} ipython3
 % import requests
-% from bs4 import BeautifulSoup
+% import bs4
 % ```
 %
 % Next, we tell Python what page we want to scrape by providing the webpage's URL in quotations to the function `requests.get` and pass it into the `BeautifulSoup` function for parsing: