full reformatting of code blocks

Author: tmorrell

_episodes/02-starting-with-data.md

@@ -578,10 +578,11 @@ total_count.plot(kind='bar');
 >  need to be in individual columns.  Here's a simple example with some data where
 >  'a', 'b', and 'c' are the groups, and 'one' and 'two' are the subgroups.
 >
-> ```
+> ~~~
 > d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
 > pd.DataFrame(d)
-> ```
+> ~~~
+> {: .language-python }
 >
 > shows the following data
 >
@@ -595,11 +596,12 @@ total_count.plot(kind='bar');
 >
 > We can plot the above with
 >
-> ```
+> ~~~
 > # Plot stacked data so columns 'one' and 'two' are stacked
 > my_df = pd.DataFrame(d)
 > my_df.plot(kind='bar',stacked=True,title="The title of my graph")
-> ```
+> ~~~
+> {: .language-python }
 >
 > ![Stacked Bar Plot](../fig/stackedBar1.png)
 >
@@ -618,7 +620,8 @@ total_count.plot(kind='bar');
 >> ```python
 >> by_site_sex = surveys_df.groupby(['plot_id','sex'])
 >> site_sex_count = by_site_sex['weight'].sum()
->> ```
+>> ~~~
+>> {: .language-python }
 >>
 >> This calculates the sums of weights for each sex within each site as a table
 >>
@@ -642,7 +645,8 @@ total_count.plot(kind='bar');
 >> by_site_sex = surveys_df.groupby(['plot_id','sex'])
 >> site_sex_count = by_site_sex['weight'].sum()
 >> site_sex_count.unstack()
->> ```
+>> ~~~
+>> {: .language-python }
 >>
 >> The `unstack` method above will display the following output:
 >>
@@ -667,7 +671,8 @@ total_count.plot(kind='bar');
 >> s_plot = spc.plot(kind='bar',stacked=True,title="Total weight by site and sex")
 >> s_plot.set_ylabel("Weight")
 >> s_plot.set_xlabel("Plot")
->> ```
+>> ~~~
+>> {: .language-python }
 >>
 >> ![Stacked Bar Plot](../fig/stackedBar.png)
 > {: .solution}

_episodes/03-index-slice-subset.md

@@ -122,23 +122,26 @@ a = [1, 2, 3, 4, 5]
 >
 > 1. What value does the code below return?
 >
->    ```python
+>    ~~~
 >    a[0]
->    ```
+>    ~~~
+>    {: .language-python }
 >
 > 2. How about this:
 >
->    ```python
+>    ~~~
 >    a[5]
->    ```
+>    ~~~
+>    {: .language-python }
 >
 > 3. In the example above, calling `a[5]` returns an error. Why is that?
 >
 > 4. What about?
 >
->    ```python
+>    ~~~
 >    a[len(a)]
->    ```
+>    ~~~
+>    {: .language-python }
 {: .challenge}
 
 
@@ -230,14 +233,16 @@ the other will see the same changes to the reference object.
 
 - **Copy** uses the dataframe's `copy()` method
 
-  ```python
+  ~~~
   true_copy_surveys_df = surveys_df.copy()
-  ```
+  ~~~
+  {: .language-python }
 - A **Reference** is created using the `=` operator
 
-  ```python
+  ~~~
   ref_surveys_df = surveys_df
-  ```
+  ~~~
+  {: .language-python }
 
 Okay, that's enough of that. Let's create a brand new clean dataframe from
 the original data CSV file.
@@ -410,9 +415,10 @@ Experiment with selecting various subsets of the "surveys" data.
 > 2. You can use the `isin` command in Python to query a DataFrame based upon a
 >   list of values as follows:
 >
->    ```python
+>    ~~~
 >    surveys_df[surveys_df['species_id'].isin([listGoesHere])]
->    ```
+>    ~~~
+>    {: .language-python }
 >
 >   Use the `isin` function to find all plots that contain particular species
 >   in the "surveys" DataFrame. How many records contain these values?

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

@@ -125,7 +125,7 @@ surveys_df.dtypes
 
 which **returns**:
 
-```
+~~~
 record_id            int64
 month                int64
 day                  int64
@@ -136,7 +136,8 @@ sex                 object
 hindfoot_length    float64
 weight             float64
 dtype: object
-```
+~~~
+{: .language-python }
 
 Note that most of the columns in our Survey data are of type `int64`. This means
 that they are 64 bit integers. But the weight column is a floating point value
@@ -222,7 +223,7 @@ surveys_df['record_id'].dtype
 
 What happened in the last challenge activity? Notice that this throws a value error:
 `ValueError: Cannot convert NA to integer`. If we look at the `weight` column in the surveys
-data we notice that there are NaN (**N**ot **a** **N**umber) values. *NaN* values are undefined
+data we notice that there are NaN (**N**ot **a** **N**umber) values. **NaN** values are undefined
 values that cannot be represented mathematically. Pandas, for example, will read
 an empty cell in a CSV or Excel sheet as a NaN. NaNs have some desirable properties: if we
 were to average the `weight` column without replacing our NaNs, Python would know to skip

_episodes/06-loops-and-functions.md

@@ -623,13 +623,14 @@ values to the function using these keywords:
 > doesn't. Add some code to your function that writes out the CSV files, to check
 > for a directory to write to.
 >
-> ```Python
+> ~~~
 >	if 'dir_name_here' in os.listdir('.'):
 >	    print('Processed directory exists')
 >	else:
 >	    os.mkdir('dir_name_here')
 >	    print('Processed directory created')
-> ```
+> ~~~
+> {: .language-python }
 >
 > 4. The code that you have written so far to loop through the years is good,
 > however it is not necessarily reproducible with different datasets.

_episodes/07-visualization-ggplot-python.md

@@ -72,6 +72,8 @@ To build a `plotnine` graphic we need to:
 As we have not defined anything else, just an empty figure is available and
 presented.
 
+As we have not defined anything else, just an empty figure is available and
+presented.
 
 - Define aesthetics (`aes`), by **selecting variables** used in the plot and
 `mapping` them to a presentation such as plotting size, shape color, etc. You

_episodes/08-putting-it-all-together.md

@@ -256,7 +256,7 @@ figure number inside the parentheses:
 {: .language-python}
 
 A single figure can also include multiple plots in a grid pattern. The
-`subplot()` command especifies the number of rows, the number of columns, and
+`subplot()` command specifies the number of rows, the number of columns, and
 the number of the space in the grid that particular plot is occupying:
 
 ~~~