minor edits

Author: tomvannuenen

lessons/3_Project.ipynb

@@ -314,7 +314,7 @@
    "source": [
     "## Subsetting a DataFrame\n",
     "\n",
-    "In order to create two bar plots of general health – for people above and below the poverty line – we can create two DataFrames for these groups. We can then plot the values in these DataFrames \"on top of\" one another in a barplot.\n",
+    "In order to create two bar plots of general health – for people above and below the poverty line – we can create two DataFrames for just these groups. We can then plot the values in these DataFrames \"on top of\" one another in a barplot.\n",
     "\n",
     "Recall that we can subset DataFrames with Boolean masks. For instance, say we have a DataFrame `counts` with a column `A`. If we want to create a new DataFrame called `above_800`, which only contains the values over 800 in column `A` of `counts`, we would write:\n",
     "\n",

solutions/3_Project.ipynb

@@ -279,7 +279,7 @@
     "## Counting Values\n",
     "The first thing we will want to do is count values of poverty levels: we want to see how many levels there are, and how the data are distributed. \n",
     "1. Run `value_counts()` on the `poverty_level` column. \n",
-    "2. <span style=\"color:purple\"> Look through the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) and **normalize** the output of `value_counts()`.</span>"
+    "2. Look through the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) and **normalize** the output of `value_counts()`."
    ]
   },
   {