Merge pull request #49 from chennesy/issue_47

remove functions with conditionals section

Author: quist00

episodes/conditionals.md

@@ -276,7 +276,7 @@ print(result)
 
 ## Processing Files Based on Record Length
 
-Modify this program so that it only processes files with fewer than 50 records.
+Modify this program so that it only processes files with fewer than 85 records.
 
 ```python
 import glob
@@ -344,58 +344,6 @@ print(smallest, largest)
 
 :::::::::::::::::::::::::
 
-::::::::::::::::::::::::::::::::::::::::::::::::::
-
-:::::::::::::::::::::::::::::::::::::::::  callout
-
-## Using Functions With Conditionals in Pandas
-
-Functions will often contain conditionals.  Here is a short example that
-will indicate which quartile the argument is in based on hand-coded values
-for the quartile cut points.
-
-```python
-def calculate_life_quartile(exp):
-    if exp < 58.41:
-        # This observation is in the first quartile
-        return 1
-    elif exp >= 58.41 and exp < 67.05:
-        # This observation is in the second quartile
-       return 2
-    elif exp >= 67.05 and exp < 71.70:
-        # This observation is in the third quartile
-       return 3
-    elif exp >= 71.70:
-        # This observation is in the fourth quartile
-       return 4
-    else:
-        # This observation has bad data
-       return None
-
-calculate_life_quartile(62.5)
-```
-
-```output
-2
-```
-
-That function would typically be used within a `for` loop, but Pandas has
-a different, more efficient way of doing the same thing, and that is by
-*applying* a function to a dataframe or a portion of a dataframe.  Here
-is an example, using the definition above.
-
-```python
-data = pd.read_csv('Americas-data.csv')
-data['life_qrtl'] = data['lifeExp'].apply(calculate_life_quartile)
-```
-
-There is a lot in that second line, so let's take it piece by piece.
-On the right side of the `=` we start with `data['lifeExp']`, which is the
-column in the dataframe called `data` labeled `lifExp`.  We use the
-`apply()` to do what it says, apply the `calculate_life_quartile` to the
-value of this column for every row in the dataframe.
-
-
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 :::::::::::::::::::::::::::::::::::::::: keypoints