cs120_lab1b_word_count_rdd.py

# Databricks notebook source exported at Fri, 8 Jul 2016 18:23:19 UTC

# MAGIC %md
# MAGIC <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/> </a> <br/> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"> Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. </a>

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# MAGIC %md
# MAGIC #![Spark Logo](http://spark-mooc.github.io/web-assets/images/ta_Spark-logo-small.png) + ![Python Logo](http://spark-mooc.github.io/web-assets/images/python-logo-master-v3-TM-flattened_small.png)
# MAGIC # Word Count Lab: Building a word count application
# MAGIC 
# MAGIC This lab will build on the techniques covered in the Spark tutorial to develop a simple word count application.  The volume of unstructured text in existence is growing dramatically, and Spark is an excellent tool for analyzing this type of data.  In this lab, we will write code that calculates the most common words in the [Complete Works of William Shakespeare](http://www.gutenberg.org/ebooks/100) retrieved from [Project Gutenberg](http://www.gutenberg.org/wiki/Main_Page).
# MAGIC 
# MAGIC This could also be scaled to find the most common words in Wikipedia.
# MAGIC 
# MAGIC ## During this lab we will cover:
# MAGIC * *Part 1:* Creating a base RDD and pair RDDs
# MAGIC * *Part 2:* Counting with pair RDDs
# MAGIC * *Part 3:* Finding unique words and a mean value
# MAGIC * *Part 4:* Apply word count to a file
# MAGIC * *Appendix A:* Submitting your exercises to the Autograder
# MAGIC 
# MAGIC > Note that for reference, you can look up the details of the relevant methods in:
# MAGIC > * [Spark's Python API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)

# COMMAND ----------

labVersion = 'cs120x-lab1b-1.0.0'

# COMMAND ----------

# MAGIC %md
# MAGIC ## Part 1: Creating a base RDD and pair RDDs

# COMMAND ----------

# MAGIC %md
# MAGIC In this part of the lab, we will explore creating a base RDD with `parallelize` and using pair RDDs to count words.

# COMMAND ----------

# MAGIC %md
# MAGIC ### (1a) Create a base RDD
# MAGIC We'll start by generating a base RDD by using a Python list and the `sc.parallelize` method.  Then we'll print out the type of the base RDD.

# COMMAND ----------

wordsList = ['cat', 'elephant', 'rat', 'rat', 'cat']
wordsRDD = sc.parallelize(wordsList, 4)
# Print out the type of wordsRDD
print type(wordsRDD)

# COMMAND ----------

# MAGIC %md
# MAGIC ### (1b) Pluralize and test
# MAGIC 
# MAGIC Let's use a `map()` transformation to add the letter 's' to each string in the base RDD we just created. We'll define a Python function that returns the word with an 's' at the end of the word.  Please replace `<FILL IN>` with your solution.  If you have trouble, the next cell has the solution.  After you have defined `makePlural` you can run the third cell which contains a test.  If you implementation is correct it will print `1 test passed`.
# MAGIC 
# MAGIC This is the general form that exercises will take, except that no example solution will be provided.  Exercises will include an explanation of what is expected, followed by code cells where one cell will have one or more `<FILL IN>` sections.  The cell that needs to be modified will have `# TODO: Replace <FILL IN> with appropriate code` on its first line.  Once the `<FILL IN>` sections are updated and the code is run, the test cell can then be run to verify the correctness of your solution.  The last code cell before the next markdown section will contain the tests.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
def makePlural(word):
    """Adds an 's' to `word`.

    Note:
        This is a simple function that only adds an 's'.  No attempt is made to follow proper
        pluralization rules.

    Args:
        word (str): A string.

    Returns:
        str: A string with 's' added to it.
    """
    return <FILL IN>

print makePlural('cat')

# COMMAND ----------

# One way of completing the function
def makePlural(word):
    return word + 's'

print makePlural('cat')

# COMMAND ----------

# Load in the testing code and check to see if your answer is correct
# If incorrect it will report back '1 test failed' for each failed test
# Make sure to rerun any cell you change before trying the test again
from databricks_test_helper import Test
# TEST Pluralize and test (1b)
Test.assertEquals(makePlural('rat'), 'rats', 'incorrect result: makePlural does not add an s')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (1c) Apply `makePlural` to the base RDD
# MAGIC 
# MAGIC Now pass each item in the base RDD into a [map()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.map) transformation that applies the `makePlural()` function to each element. And then call the [collect()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.collect) action to see the transformed RDD.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
pluralRDD = wordsRDD.map(<FILL IN>)
print pluralRDD.collect()

# COMMAND ----------

# TEST Apply makePlural to the base RDD(1c)
Test.assertEquals(pluralRDD.collect(), ['cats', 'elephants', 'rats', 'rats', 'cats'],
                  'incorrect values for pluralRDD')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (1d) Pass a `lambda` function to `map`
# MAGIC 
# MAGIC Let's create the same RDD using a `lambda` function.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
pluralLambdaRDD = wordsRDD.map(lambda <FILL IN>)
print pluralLambdaRDD.collect()

# COMMAND ----------

# TEST Pass a lambda function to map (1d)
Test.assertEquals(pluralLambdaRDD.collect(), ['cats', 'elephants', 'rats', 'rats', 'cats'],
                  'incorrect values for pluralLambdaRDD (1d)')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (1e) Length of each word
# MAGIC 
# MAGIC Now use `map()` and a `lambda` function to return the number of characters in each word.  We'll `collect` this result directly into a variable.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
pluralLengths = (pluralRDD
                 <FILL IN>
                 .collect())
print pluralLengths

# COMMAND ----------

# TEST Length of each word (1e)
Test.assertEquals(pluralLengths, [4, 9, 4, 4, 4],
                  'incorrect values for pluralLengths')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (1f) Pair RDDs
# MAGIC 
# MAGIC The next step in writing our word counting program is to create a new type of RDD, called a pair RDD. A pair RDD is an RDD where each element is a pair tuple `(k, v)` where `k` is the key and `v` is the value. In this example, we will create a pair consisting of `('<word>', 1)` for each word element in the RDD.
# MAGIC We can create the pair RDD using the `map()` transformation with a `lambda()` function to create a new RDD.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
wordPairs = wordsRDD.<FILL IN>
print wordPairs.collect()

# COMMAND ----------

# TEST Pair RDDs (1f)
Test.assertEquals(wordPairs.collect(),
                  [('cat', 1), ('elephant', 1), ('rat', 1), ('rat', 1), ('cat', 1)],
                  'incorrect value for wordPairs')

# COMMAND ----------

# MAGIC %md
# MAGIC ## Part 2: Counting with pair RDDs

# COMMAND ----------

# MAGIC %md
# MAGIC Now, let's count the number of times a particular word appears in the RDD. There are multiple ways to perform the counting, but some are much less efficient than others.
# MAGIC 
# MAGIC A naive approach would be to `collect()` all of the elements and count them in the driver program. While this approach could work for small datasets, we want an approach that will work for any size dataset including terabyte- or petabyte-sized datasets. In addition, performing all of the work in the driver program is slower than performing it in parallel in the workers. For these reasons, we will use data parallel operations.

# COMMAND ----------

# MAGIC %md
# MAGIC ### (2a) `groupByKey()` approach
# MAGIC An approach you might first consider (we'll see shortly that there are better ways) is based on using the [groupByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.groupByKey) transformation. As the name implies, the `groupByKey()` transformation groups all the elements of the RDD with the same key into a single list in one of the partitions.
# MAGIC 
# MAGIC There are two problems with using `groupByKey()`:
# MAGIC   + The operation requires a lot of data movement to move all the values into the appropriate partitions.
# MAGIC   + The lists can be very large. Consider a word count of English Wikipedia: the lists for common words (e.g., the, a, etc.) would be huge and could exhaust the available memory in a worker.
# MAGIC 
# MAGIC Use `groupByKey()` to generate a pair RDD of type `('word', iterator)`.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
# Note that groupByKey requires no parameters
wordsGrouped = wordPairs.<FILL IN>
for key, value in wordsGrouped.collect():
    print '{0}: {1}'.format(key, list(value))

# COMMAND ----------

# TEST groupByKey() approach (2a)
Test.assertEquals(sorted(wordsGrouped.mapValues(lambda x: list(x)).collect()),
                  [('cat', [1, 1]), ('elephant', [1]), ('rat', [1, 1])],
                  'incorrect value for wordsGrouped')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (2b) Use `groupByKey()` to obtain the counts
# MAGIC 
# MAGIC Using the `groupByKey()` transformation creates an RDD containing 3 elements, each of which is a pair of a word and a Python iterator.
# MAGIC 
# MAGIC Now sum the iterator using a `map()` transformation.  The result should be a pair RDD consisting of (word, count) pairs.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
wordCountsGrouped = wordsGrouped.<FILL IN>
print wordCountsGrouped.collect()

# COMMAND ----------

# TEST Use groupByKey() to obtain the counts (2b)
Test.assertEquals(sorted(wordCountsGrouped.collect()),
                  [('cat', 2), ('elephant', 1), ('rat', 2)],
                  'incorrect value for wordCountsGrouped')


# COMMAND ----------

# MAGIC %md
# MAGIC ** (2c) Counting using `reduceByKey` **
# MAGIC 
# MAGIC A better approach is to start from the pair RDD and then use the [reduceByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.reduceByKey) transformation to create a new pair RDD. The `reduceByKey()` transformation gathers together pairs that have the same key and applies the function provided to two values at a time, iteratively reducing all of the values to a single value. `reduceByKey()` operates by applying the function first within each partition on a per-key basis and then across the partitions, allowing it to scale efficiently to large datasets.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
# Note that reduceByKey takes in a function that accepts two values and returns a single value
wordCounts = wordPairs.reduceByKey(<FILL IN>)
print wordCounts.collect()

# COMMAND ----------

# TEST Counting using reduceByKey (2c)
Test.assertEquals(sorted(wordCounts.collect()), [('cat', 2), ('elephant', 1), ('rat', 2)],
                  'incorrect value for wordCounts')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (2d) All together
# MAGIC 
# MAGIC The expert version of the code performs the `map()` to pair RDD, `reduceByKey()` transformation, and `collect` in one statement.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
wordCountsCollected = (wordsRDD
                       <FILL IN>
                       .collect())
print wordCountsCollected

# COMMAND ----------

# TEST All together (2d)
Test.assertEquals(sorted(wordCountsCollected), [('cat', 2), ('elephant', 1), ('rat', 2)],
                  'incorrect value for wordCountsCollected')


# COMMAND ----------

# MAGIC %md
# MAGIC ## Part 3: Finding unique words and a mean value

# COMMAND ----------

# MAGIC %md
# MAGIC ### (3a) Unique words
# MAGIC 
# MAGIC Calculate the number of unique words in `wordsRDD`.  You can use other RDDs that you have already created to make this easier.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
uniqueWords = <FILL IN>
print uniqueWords

# COMMAND ----------

# TEST Unique words (3a)
Test.assertEquals(uniqueWords, 3, 'incorrect count of uniqueWords')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (3b) Mean using `reduce`
# MAGIC 
# MAGIC Find the mean number of words per unique word in `wordCounts`.
# MAGIC 
# MAGIC Use a `reduce()` action to sum the counts in `wordCounts` and then divide by the number of unique words.  First `map()` the pair RDD `wordCounts`, which consists of (key, value) pairs, to an RDD of values.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
from operator import add
totalCount = (wordCounts
              .map(<FILL IN>)
              .reduce(<FILL IN>))
average = totalCount / float(<FILL IN>)
print totalCount
print round(average, 2)

# COMMAND ----------

# TEST Mean using reduce (3b)
Test.assertEquals(round(average, 2), 1.67, 'incorrect value of average')

# COMMAND ----------

# MAGIC %md
# MAGIC ## Part 4: Apply word count to a file

# COMMAND ----------

# MAGIC %md
# MAGIC In this section we will finish developing our word count application.  We'll have to build the `wordCount` function, deal with real world problems like capitalization and punctuation, load in our data source, and compute the word count on the new data.

# COMMAND ----------

# MAGIC %md
# MAGIC ### (4a) `wordCount` function
# MAGIC 
# MAGIC First, define a function for word counting.  You should reuse the techniques that have been covered in earlier parts of this lab.  This function should take in an RDD that is a list of words like `wordsRDD` and return a pair RDD that has all of the words and their associated counts.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
def wordCount(wordListRDD):
    """Creates a pair RDD with word counts from an RDD of words.

    Args:
        wordListRDD (RDD of str): An RDD consisting of words.

    Returns:
        RDD of (str, int): An RDD consisting of (word, count) tuples.
    """
    <FILL IN>
print wordCount(wordsRDD).collect()

# COMMAND ----------

# TEST wordCount function (4a)
Test.assertEquals(sorted(wordCount(wordsRDD).collect()),
                  [('cat', 2), ('elephant', 1), ('rat', 2)],
                  'incorrect definition for wordCount function')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (4b) Capitalization and punctuation
# MAGIC 
# MAGIC Real world files are more complicated than the data we have been using in this lab. Some of the issues we have to address are:
# MAGIC   + Words should be counted independent of their capitialization (e.g., Spark and spark should be counted as the same word).
# MAGIC   + All punctuation should be removed.
# MAGIC   + Any leading or trailing spaces on a line should be removed.
# MAGIC 
# MAGIC Define the function `removePunctuation` that converts all text to lower case, removes any punctuation, and removes leading and trailing spaces.  Use the Python [re](https://docs.python.org/2/library/re.html) module to remove any text that is not a letter, number, or space. Reading `help(re.sub)` might be useful.
# MAGIC If you are unfamiliar with regular expressions, you may want to review [this tutorial](https://developers.google.com/edu/python/regular-expressions) from Google.  Also, [this website](https://regex101.com/#python) is  a great resource for debugging your regular expression.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
import re
def removePunctuation(text):
    """Removes punctuation, changes to lower case, and strips leading and trailing spaces.

    Note:
        Only spaces, letters, and numbers should be retained.  Other characters should should be
        eliminated (e.g. it's becomes its).  Leading and trailing spaces should be removed after
        punctuation is removed.

    Args:
        text (str): A string.

    Returns:
        str: The cleaned up string.
    """
    <FILL IN>
print removePunctuation('Hi, you!')
print removePunctuation(' No under_score!')
print removePunctuation(' *      Remove punctuation then spaces  * ')

# COMMAND ----------

# TEST Capitalization and punctuation (4b)
Test.assertEquals(removePunctuation(" The Elephant's 4 cats. "),
                  'the elephants 4 cats',
                  'incorrect definition for removePunctuation function')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (4c) Load a text file
# MAGIC 
# MAGIC For the next part of this lab, we will use the [Complete Works of William Shakespeare](http://www.gutenberg.org/ebooks/100) from [Project Gutenberg](http://www.gutenberg.org/wiki/Main_Page). To convert a text file into an RDD, we use the `SparkContext.textFile()` method. We also apply the recently defined `removePunctuation()` function using a `map()` transformation to strip out the punctuation and change all text to lower case.  Since the file is large we use `take(15)`, so that we only print 15 lines.

# COMMAND ----------

# MAGIC %fs

# COMMAND ----------

# Just run this code
import os.path
fileName = "dbfs:/" + os.path.join('databricks-datasets', 'cs100', 'lab1', 'data-001', 'shakespeare.txt')

shakespeareRDD = sc.textFile(fileName, 8).map(removePunctuation)
print '\n'.join(shakespeareRDD
                .zipWithIndex()  # to (line, lineNum)
                .map(lambda (l, num): '{0}: {1}'.format(num, l))  # to 'lineNum: line'
                .take(15))

# COMMAND ----------

# MAGIC %md
# MAGIC ### (4d) Words from lines
# MAGIC 
# MAGIC Before we can use the `wordcount()` function, we have to address two issues with the format of the RDD:
# MAGIC   + The first issue is that  that we need to split each line by its spaces. ** Performed in (4d). **
# MAGIC   + The second issue is we need to filter out empty lines. ** Performed in (4e). **
# MAGIC 
# MAGIC Apply a transformation that will split each element of the RDD by its spaces. For each element of the RDD, you should apply Python's string [split()](https://docs.python.org/2/library/string.html#string.split) function. You might think that a `map()` transformation is the way to do this, but think about what the result of the `split()` function will be.
# MAGIC 
# MAGIC > Note:
# MAGIC > * Do not use the default implemenation of `split()`, but pass in a separator value.  For example, to split `line` by commas you would use `line.split(',')`.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
shakespeareWordsRDD = shakespeareRDD.<FILL_IN>
shakespeareWordCount = shakespeareWordsRDD.count()
print shakespeareWordsRDD.top(5)
print shakespeareWordCount

# COMMAND ----------

# TEST Words from lines (4d)
# This test allows for leading spaces to be removed either before or after
# punctuation is removed.
Test.assertTrue(shakespeareWordCount == 927631 or shakespeareWordCount == 928908,
                'incorrect value for shakespeareWordCount')
Test.assertEquals(shakespeareWordsRDD.top(5),
                  [u'zwaggerd', u'zounds', u'zounds', u'zounds', u'zounds'],
                  'incorrect value for shakespeareWordsRDD')

# COMMAND ----------

# MAGIC %md
# MAGIC ** (4e) Remove empty elements **
# MAGIC 
# MAGIC The next step is to filter out the empty elements.  Remove all entries where the word is `''`.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
shakeWordsRDD = shakespeareWordsRDD.<FILL_IN>
shakeWordCount = shakeWordsRDD.count()
print shakeWordCount

# COMMAND ----------

# TEST Remove empty elements (4e)
Test.assertEquals(shakeWordCount, 882996, 'incorrect value for shakeWordCount')

# COMMAND ----------

# MAGIC %md
# MAGIC ### (4f) Count the words
# MAGIC 
# MAGIC We now have an RDD that is only words.  Next, let's apply the `wordCount()` function to produce a list of word counts. We can view the top 15 words by using the `takeOrdered()` action; however, since the elements of the RDD are pairs, we need a custom sort function that sorts using the value part of the pair.
# MAGIC 
# MAGIC You'll notice that many of the words are common English words. These are called stopwords. In a later lab, we will see how to eliminate them from the results.
# MAGIC Use the `wordCount()` function and `takeOrdered()` to obtain the fifteen most common words and their counts.

# COMMAND ----------

# TODO: Replace <FILL IN> with appropriate code
top15WordsAndCounts = <FILL IN>
print '\n'.join(map(lambda (w, c): '{0}: {1}'.format(w, c), top15WordsAndCounts))

# COMMAND ----------

# TEST Count the words (4f)
Test.assertEquals(top15WordsAndCounts,
                  [(u'the', 27361), (u'and', 26028), (u'i', 20681), (u'to', 19150), (u'of', 17463),
                   (u'a', 14593), (u'you', 13615), (u'my', 12481), (u'in', 10956), (u'that', 10890),
                   (u'is', 9134), (u'not', 8497), (u'with', 7771), (u'me', 7769), (u'it', 7678)],
                  'incorrect value for top15WordsAndCounts')

# COMMAND ----------

# MAGIC %md
# MAGIC ## Appendix A: Submitting Your Exercises to the Autograder
# MAGIC 
# MAGIC This section guides you through Step 2 of the grading process ("Submit to Autograder").
# MAGIC 
# MAGIC Once you confirm that your lab notebook is passing all tests, you can submit it first to the course autograder and then second to the edX website to receive a grade.
# MAGIC 
# MAGIC ** Note that you can only submit to the course autograder once every 1 minute. **

# COMMAND ----------

# MAGIC %md
# MAGIC ### Step 2(a): Restart your cluster by clicking on the dropdown next to your cluster name and selecting "Restart Cluster".
# MAGIC 
# MAGIC You can do this step in either notebook, since there is one cluster for your notebooks.
# MAGIC 
# MAGIC <img src="http://spark-mooc.github.io/web-assets/images/submit_restart.png" alt="Drawing" />

# COMMAND ----------

# MAGIC %md
# MAGIC ### Step 2(b): _IN THIS NOTEBOOK_, click on "Run All" to run all of the cells.
# MAGIC 
# MAGIC <img src="http://spark-mooc.github.io/web-assets/images/submit_runall.png" alt="Drawing" style="height: 80px"/>
# MAGIC 
# MAGIC This step will take some time.
# MAGIC 
# MAGIC Wait for your cluster to finish running the cells in your lab notebook before proceeding.

# COMMAND ----------

# MAGIC %md
# MAGIC ### Step 2(c): Publish this notebook
# MAGIC 
# MAGIC Publish _this_ notebook by clicking on the "Publish" button at the top.
# MAGIC 
# MAGIC <img src="http://spark-mooc.github.io/web-assets/images/Lab0_Publish0.png" alt="Drawing" style="height: 150px"/>
# MAGIC 
# MAGIC When you click on the button, you will see the following popup.
# MAGIC 
# MAGIC <img src="http://spark-mooc.github.io/web-assets/images/Lab0_Publish1.png" alt="Drawing" />
# MAGIC 
# MAGIC When you click on "Publish", you will see a popup with your notebook's public link. **Copy the link and set the `notebook_URL` variable in the AUTOGRADER notebook (not this notebook).**
# MAGIC 
# MAGIC <img src="http://spark-mooc.github.io/web-assets/images/Lab0_Publish2.png" alt="Drawing" />

# COMMAND ----------

# MAGIC %md
# MAGIC ### Step 2(d): Set the notebook URL and Lab ID in the Autograder notebook, and run it
# MAGIC 
# MAGIC Go to the Autograder notebook and paste the link you just copied into it, so that it is assigned to the `notebook_url` variable.
# MAGIC 
# MAGIC ```
# MAGIC notebook_url = "..." # put your URL here
# MAGIC ```
# MAGIC 
# MAGIC Then, find the line that looks like this:
# MAGIC 
# MAGIC ```
# MAGIC lab = <FILL IN>
# MAGIC ```
# MAGIC and change `<FILL IN>` to "CS120x-lab1b":
# MAGIC 
# MAGIC ```
# MAGIC lab = "CS120x-lab1b"
# MAGIC ```
# MAGIC 
# MAGIC Then, run the Autograder notebook to submit your lab.

# COMMAND ----------

# MAGIC %md
# MAGIC ### <img src="http://spark-mooc.github.io/web-assets/images/oops.png" style="height: 200px"/> If things go wrong
# MAGIC 
# MAGIC It's possible that your notebook looks fine to you, but fails in the autograder. (This can happen when you run cells out of order, as you're working on your notebook.) If that happens, just try again, starting at the top of Appendix A.