04-data-types-and-format.md: split and fix code/output cells

Author: maxim-belkin

_episodes/04-data-types-and-format.md

@@ -3,8 +3,8 @@ title: Data Types and Formats
 teaching: 20
 exercises: 25
 questions:
-  - "What types of data can be contained in a DataFrame?"
-  - "Why is the data type important?"
+    - "What types of data can be contained in a DataFrame?"
+    - "Why is the data type important?"
 objectives:
     - "Describe how information is stored in a Python DataFrame."
     - "Define the two main types of data in Python: text and numerics."
@@ -32,7 +32,7 @@ numeric data, you get an error.
 In this lesson we will review ways to explore and better understand the
 structure and format of our data.
 
-# Types of Data
+## Types of Data
 
 How information is stored in a
 DataFrame or a Python object affects what we can do with it and the outputs of
@@ -170,26 +170,48 @@ subtraction, division and multiplication work on floats and integers as we'd exp
 
 ~~~
 print(5+5)
+~~~
+{: .language-python}
+
+~~~
 10
+~~~
+{: .output}
 
+~~~
 print(24-4)
-20
 ~~~
 {: .language-python}
 
+~~~
+20
+~~~
+{: .output}
+
 If we divide one integer by another, we get a float.
 The result on Python 3 is different than in Python 2, where the result is an
 integer (integer division).
 
 ~~~
 print(5/9)
+~~~
+{: .language-python}
+
+~~~
 0.5555555555555556
+~~~
+{: .output}
 
+~~~
 print(10/3)
-3.3333333333333335
 ~~~
 {: .language-python}
 
+~~~
+3.3333333333333335
+~~~
+{: .output}
+
 We can also convert a floating point number to an integer or an integer to
 floating point number. Notice that Python by default rounds down when it
 converts from floating point to integer.
@@ -198,16 +220,28 @@ converts from floating point to integer.
 # Convert a to an integer
 a = 7.83
 int(a)
+~~~
+{: .language-python}
+
+~~~
 7
+~~~
+{: .output}
 
+~~~
 # Convert b to a float
 b = 7
 float(b)
-7.0
 ~~~
 {: .language-python}
 
-# Working With Our Survey Data
+~~~
+7.0
+~~~
+{: .output}
+
+
+## Working With Our Survey Data
 
 Getting back to our data, we can modify the format of values within our data, if
 we want. For instance, we could convert the `record_id` field to floating point
@@ -250,9 +284,14 @@ over those cells.
 
 ~~~
 surveys_df['weight'].mean()
-42.672428212991356
 ~~~
 {: .language-python}
+
+~~~
+42.672428212991356
+~~~
+{: .output}
+
 Dealing with missing data values is always a challenge. It's sometimes hard to
 know why values are missing - was it because of a data entry error? Or data that
 someone was unable to collect? Should the value be 0? We need to know how
@@ -298,10 +337,14 @@ out or ignored.
 
 ~~~
 df1['weight'].mean()
-38.751976145601844
 ~~~
 {: .language-python}
 
+~~~
+38.751976145601844
+~~~
+{: .output}
+
 We can fill NaN values with any value that we chose. The code below fills all
 NaN values with a mean for all weight values.
 
@@ -342,7 +385,6 @@ By default, dropna removes rows that contain missing data for even just one colu
 
 ~~~
 df_na = surveys_df.dropna()
-
 ~~~
 {: .language-python}