Updated lesson notebook to be consistent with lesson changes

Author: davewhipp

notebooks/L6/numpy/Advanced-data-processing-with-NumPy.ipynb

@@ -280,10 +280,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Calculations within a date range\n",
+    "## Calculations within a date range\n",
     "\n",
     "One common thing we'd like to do is calculate something about our data over a fixed time range, for example, one year.\n",
     "We've seen this idea previously, but just to remind you we can do this by masking our data.\n",
+    "\n",
+    "### Removing missing data\n",
+    "\n",
     "First, let's check for any missing data in the `tmax` array."
    ]
   },
@@ -423,9 +426,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Splitting dates into separate year, month, day\n",
+    "## Splitting dates into separate year, month, and day\n",
     "\n",
     "Now we can go about splitting our dates into separate arrays for the year, month, and day.\n",
+    "\n",
+    "### Splitting a single date string\n",
+    "\n",
     "As a reminder, it is first useful to recall that character strings can be sliced using index values just like arrays."
    ]
   },
@@ -462,6 +468,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Splitting dates in a NumPy array\n",
+    "\n",
     "With this in mind, we can now attempt the same in our NumPy array `date_clean_str`."
    ]
   },
@@ -534,9 +542,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Finding the average monthly max temperature\n",
+    "## Finding averages in a date range\n",
     "\n",
     "Now that we have separate arrays for the year, month, and day we can do some fun stuff like calculate the monthly average temperature for a given year.\n",
+    "\n",
+    "### Finding the average monthly max temperature\n",
+    "\n",
     "Let's take 2010 again as our example and find the average temperatures for each month in 2010.\n",
     "For this, we can use a `for` loop."
    ]
@@ -571,7 +582,7 @@
     "1. We created an array of zeros with 12 locations to store the monthly average temperatures.\n",
     "2. We used an index variable called `index` to define where to store those monthly average temperatures. We could have done this differently, but there is a good reason to use `index` here because it is more flexible if we wanted to consider more than one year of data as you'll see in this week's exercise.\n",
     "3. We then used a `for` loop to go through each unique month (found using `np.unique()`).\n",
-    "4. For each month we found the max temperature using the condition `month == month_now`, and taking the `tmax_clean.mean()` for that month.\n",
+    "4. For each month we found the max temperature using the condition `month == month_now`, and taking the `tmax_clean.mean()` for that month. We also limit the months to those in 2010 by using the `year == '2010'` condition.\n",
     "5. We added `1` to the `index` value to ensure the next mean value goes into the correct location in the `means_2010` array.\n",
     "\n",
     "This might seem a bit complicated, but it is rather powerful as well.\n",
@@ -709,7 +720,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Comparing monthly temperatures\n",
+    "### Calculating temperature anomalies\n",
     "\n",
     "One thing we can do now that we have a mean temperature for February in 2010-2015 is to compare one of those years to the average max temperature in the time period.\n",
     "This would give us a temperature anomaly, the difference between the mean value and a given year.\n",
@@ -734,7 +745,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, February of 2010 was 1.7° Fahrenheit warmer than the average February between 2010 and 2015.\n",
+    "As we can see, February of 2010 was only 0.08° Fahrenheit warmer than the average February between 2010 and 2015.\n",
+    "Pretty typical February, it would seem.\n",
     "We'll explore exactly this kind of thing in more detail in this week's exercise."
    ]
   }