minor edits

Author: tomvannuenen

images/for.png

@@ -207,18 +207,11 @@
    "source": [
     "## Basic Function Syntax\n",
     "\n",
-    "Writing a function in Python is pretty easy! You need to know a few things:\n",
+    "Writing a function in Python is pretty easy! Let's take a look at a simple function that converts feet into meters:\n",
     "\n",
-    "*   Functions begin with the keyword `def`.\n",
-    "*   This keyword is followed by the function *name*.\n",
-    "    *   The name must obey the same rules as variable names.\n",
-    "*   The **arguments** or **parameters** are defined in parentheses as variable names.\n",
-    "    *   Use empty parentheses if the function doesn't take any inputs.\n",
-    "*   A colon indicates the end of the function *signature* (the first line).\n",
-    "*   An indented block of code denotes the start of the *body*.\n",
-    "*   The final line should be a `return` statement with the value(s) to be returned from the function.\n",
+    "<img src=\"../images/functions.png\" alt=\"Aspects of a Python Function\" width=\"700\"/>\n",
     "\n",
-    "Let's take a look at a simple function that converts feet into meters:"
+    "Here's the same function written out:"
    ]
   },
   {
@@ -313,7 +306,7 @@
    "metadata": {},
    "source": [
     "Here's what's happening to our DataFrame when we perform that operation:\n",
-    "<img src=\"../images/vectorized.png\" alt=\"Vectorization in Pandas with scalar\" width=\"500\"/>"
+    "<img src=\"../images/vectorized.png\" alt=\"Vectorization in Pandas with scalar\" width=\"600\"/>"
    ]
   },
   {
@@ -322,7 +315,7 @@
    "source": [
     "## How to `apply()` a Function\n",
     "\n",
-    "Similarly, the Pandas `.apply()` method allows you to apply a function over an entire column of a dataframe. Here's an example: "
+    "We can also use a more general technique: `apply()`. `apply()` takes any function and **applies** it over every entry of the column. Let's see how we can do the same operation as above using `apply()`."
    ]
   },
   {
@@ -341,7 +334,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the code above, we create a function that adds 10 to whatever comes into it.\n",
+    "In the code above, we create a function that adds 10 to whatever is passed into it as an argument.\n",
     "\n",
     "🔔 <span style=\"color:purple\"> **Question**: What happens when we `apply()` our function to the `pop` column?</span>"
    ]

images/functions.png

@@ -53,7 +53,7 @@
     "\n",
     "# Iteration: Loops\n",
     "\n",
-    "The strength of using computers is their speed. We can leverage this through iteration – using **loops**. \n",
+    "The strength of using computers is their speed. We can leverage this through repeated computation, also called iteration. In Python, we can do this using **loops**. \n",
     "\n",
     "A **[for loop](https://www.w3schools.com/python/python_for_loops.asp)** executes some statements once *for* each value in an interable (like a list or a string). It says: \"*for* each thing in this group, *do* these operations\".\n",
     "\n",
@@ -155,7 +155,7 @@
    "source": [
     "## Conditionals and Loops\n",
     "\n",
-    "Recall that we can use `if`-statements to check if a condition is `True` or `False`. Also recall that `True` and `False` are called **Booleans values**.\n",
+    "Recall that we can use `if`-statements to check if a condition is `True` or `False`. Also recall that `True` and `False` are called **Boolean values**.\n",
     "\n",
     "Conditionals are particularly useful when we're iterating through a list, and want to perform some operation only on specific components of that list that satisfy a certain condition."
    ]
@@ -179,6 +179,8 @@
    "source": [
     "## Aggregating Values With Loops\n",
     "\n",
+    "In the above example, we are operating on each value in `numbers`. However, instead of simply printing the results, we often will want to save them somehow. We can do this with an **accumulator variable**.\n",
+    "\n",
     "A common strategy in programs is to:\n",
     "1.  Initialize an *accumulator* variable appropriate to the datatype of the output:\n",
     "    * `int` : `0`\n",
@@ -358,7 +360,7 @@
    "source": [
     "Here's what's happening to our DataFrame when we perform that operation:\n",
     "\n",
-    "<img src=\"../images/vectorized2.png\" alt=\"Vectorizing in Pandas – multiplying columns\" width=\"500\"/>"
+    "<img src=\"../images/vectorized2.png\" alt=\"Vectorizing in Pandas – multiplying columns\" width=\"600\"/>"
    ]
   },
   {
@@ -482,6 +484,8 @@
    "source": [
     "## Sorting Values\n",
     "\n",
+    "Let's say we want to find the countries with the highest `gdpPercap`.\n",
+    "\n",
     "If we want to sort the values in a DataFrame we can use the [`sort_values()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html) method on a DataFrame. It takes as an argument the column we want to sort the DataFrame on. \n",
     "\n",
     "⚠️ **Warning:** By default, `sort_values()` sorts in **ascending order**."